In [1]:

    

from corpus_characterizer import generator_chunker
import numpy as np
import os
# from sklearn.metrics import mean_squared_error,mean_absolute_error, median_absolute_error, mean_squared_log_error, explained_variance_score, r2_score
from sklearn.metrics import mean_squared_error, mean_absolute_error, median_absolute_error, explained_variance_score, \
    r2_score
import pandas as pd
from scipy.stats import describe, kurtosistest, skewtest, normaltest
from Conv1D_LSTM_Ensemble import pair_generator_1dconv_lstm_bagged
from Conv1D_ActivationSearch_BigLoop import pair_generator_1dconv_lstm #NOT BAGGED
from AuxRegressor import create_testing_set, create_training_set
import matplotlib.pyplot as plt 

# @@@@@@@@@@@@@@ RELATIVE PATHS @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
Base_Path = "./"
image_path = "./images/"
train_path = "./train/"
test_path = "./test/"
analysis_path = "./analysis/"
chunker_path = analysis_path + "chunker/"
preds_path = analysis_path + "preds/"

#create the data-pair filenames (using zip), use the helper methods
train_set_filenames = create_training_set()
test_set_filenames = create_testing_set()
print(train_set_filenames)
print(test_set_filenames)

# for files in test_set_filenames:
#     data_load_path = test_path + '/data/' + files[0]
#     label_load_path = test_path + '/label/' + files[1]
#     # print("data/label load path: {} \n {}".format(data_load_path,label_load_path))
#     test_array = np.load(data_load_path)
#     test_label_array = np.load(label_load_path)[:, 1:]


    

    




Using Theano backend.






    




after shuffling: [('sequence_1d_239_20_fv1c.npy', 'sequence_1d_239_20_label_fv1c.npy'), ('sequence_1k_327_8_fv1c.npy', 'sequence_1k_327_8_label_fv1c.npy'), ('sequence_2c_514_5_fv1c.npy', 'sequence_2c_514_5_label_fv1c.npy'), ('sequence_2c_517_8_fv1c.npy', 'sequence_2c_517_8_label_fv1c.npy'), ('sequence_1c_100_1_fv1c.npy', 'sequence_1c_100_1_label_fv1c.npy'), ('sequence_1c_65_6_fv1c.npy', 'sequence_1c_65_6_label_fv1c.npy'), ('sequence_3a_677_19_fv1c.npy', 'sequence_3a_677_19_label_fv1c.npy'), ('sequence_1b_11_2_fv1c.npy', 'sequence_1b_11_2_label_fv1c.npy'), ('sequence_1d_192_3_fv1c.npy', 'sequence_1d_192_3_label_fv1c.npy'), ('sequence_3a_601_3_fv1c.npy', 'sequence_3a_601_3_label_fv1c.npy'), ('sequence_1d_180_21_fv1c.npy', 'sequence_1d_180_21_label_fv1c.npy'), ('sequence_2c_550_1_fv1c.npy', 'sequence_2c_550_1_label_fv1c.npy'), ('sequence_1d_121_2_fv1c.npy', 'sequence_1d_121_2_label_fv1c.npy'), ('sequence_1k_306_7_fv1c.npy', 'sequence_1k_306_7_label_fv1c.npy'), ('sequence_3a_610_12_fv1c.npy', 'sequence_3a_610_12_label_fv1c.npy'), ('sequence_1l_414_5_fv1c.npy', 'sequence_1l_414_5_label_fv1c.npy'), ('sequence_3a_701_3_fv1c.npy', 'sequence_3a_701_3_label_fv1c.npy'), ('sequence_1d_264_15_fv1c.npy', 'sequence_1d_264_15_label_fv1c.npy'), ('sequence_2c_542_3_fv1c.npy', 'sequence_2c_542_3_label_fv1c.npy'), ('sequence_1b_16_7_fv1c.npy', 'sequence_1b_16_7_label_fv1c.npy'), ('sequence_1k_357_8_fv1c.npy', 'sequence_1k_357_8_label_fv1c.npy'), ('sequence_2b_496_7_fv1c.npy', 'sequence_2b_496_7_label_fv1c.npy'), ('sequence_1d_219_30_fv1c.npy', 'sequence_1d_219_30_label_fv1c.npy'), ('sequence_1k_345_6_fv1c.npy', 'sequence_1k_345_6_label_fv1c.npy'), ('sequence_2b_467_8_fv1c.npy', 'sequence_2b_467_8_label_fv1c.npy'), ('sequence_3a_579_1_fv1c.npy', 'sequence_3a_579_1_label_fv1c.npy'), ('sequence_1l_392_3_fv1c.npy', 'sequence_1l_392_3_label_fv1c.npy'), ('sequence_1b_24_5_fv1c.npy', 'sequence_1b_24_5_label_fv1c.npy'), ('sequence_3a_700_2_fv1c.npy', 'sequence_3a_700_2_label_fv1c.npy'), ('sequence_1d_175_16_fv1c.npy', 'sequence_1d_175_16_label_fv1c.npy'), ('sequence_2b_462_3_fv1c.npy', 'sequence_2b_462_3_label_fv1c.npy'), ('sequence_2b_484_5_fv1c.npy', 'sequence_2b_484_5_label_fv1c.npy'), ('sequence_1d_186_27_fv1c.npy', 'sequence_1d_186_27_label_fv1c.npy'), ('sequence_1c_62_3_fv1c.npy', 'sequence_1c_62_3_label_fv1c.npy'), ('sequence_1d_176_17_fv1c.npy', 'sequence_1d_176_17_label_fv1c.npy'), ('sequence_1d_223_4_fv1c.npy', 'sequence_1d_223_4_label_fv1c.npy'), ('sequence_3a_665_7_fv1c.npy', 'sequence_3a_665_7_label_fv1c.npy'), ('sequence_1c_80_1_fv1c.npy', 'sequence_1c_80_1_label_fv1c.npy'), ('sequence_1d_173_14_fv1c.npy', 'sequence_1d_173_14_label_fv1c.npy'), ('sequence_1d_185_26_fv1c.npy', 'sequence_1d_185_26_label_fv1c.npy'), ('sequence_1c_81_2_fv1c.npy', 'sequence_1c_81_2_label_fv1c.npy'), ('sequence_1b_13_4_fv1c.npy', 'sequence_1b_13_4_label_fv1c.npy'), ('sequence_1d_208_19_fv1c.npy', 'sequence_1d_208_19_label_fv1c.npy'), ('sequence_1b_43_4_fv1c.npy', 'sequence_1b_43_4_label_fv1c.npy'), ('sequence_3a_706_8_fv1c.npy', 'sequence_3a_706_8_label_fv1c.npy'), ('sequence_3a_682_4_fv1c.npy', 'sequence_3a_682_4_label_fv1c.npy'), ('sequence_1d_244_25_fv1c.npy', 'sequence_1d_244_25_label_fv1c.npy'), ('sequence_1d_203_14_fv1c.npy', 'sequence_1d_203_14_label_fv1c.npy'), ('sequence_3a_603_5_fv1c.npy', 'sequence_3a_603_5_label_fv1c.npy'), ('sequence_1d_184_25_fv1c.npy', 'sequence_1d_184_25_label_fv1c.npy'), ('sequence_1c_75_6_fv1c.npy', 'sequence_1c_75_6_label_fv1c.npy'), ('sequence_3a_692_14_fv1c.npy', 'sequence_3a_692_14_label_fv1c.npy'), ('sequence_1b_6_7_fv1c.npy', 'sequence_1b_6_7_label_fv1c.npy'), ('sequence_2c_515_6_fv1c.npy', 'sequence_2c_515_6_label_fv1c.npy'), ('sequence_2b_453_4_fv1c.npy', 'sequence_2b_453_4_label_fv1c.npy'), ('sequence_1k_321_2_fv1c.npy', 'sequence_1k_321_2_label_fv1c.npy'), ('sequence_1l_413_4_fv1c.npy', 'sequence_1l_413_4_label_fv1c.npy'), ('sequence_1l_375_6_fv1c.npy', 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('sequence_1d_165_6_fv1c.npy', 'sequence_1d_165_6_label_fv1c.npy'), ('sequence_1d_210_21_fv1c.npy', 'sequence_1d_210_21_label_fv1c.npy'), ('sequence_3a_636_18_fv1c.npy', 'sequence_3a_636_18_label_fv1c.npy'), ('sequence_3a_564_5_fv1c.npy', 'sequence_3a_564_5_label_fv1c.npy'), ('sequence_1b_35_6_fv1c.npy', 'sequence_1b_35_6_label_fv1c.npy'), ('sequence_3a_642_4_fv1c.npy', 'sequence_3a_642_4_label_fv1c.npy'), ('sequence_1d_204_15_fv1c.npy', 'sequence_1d_204_15_label_fv1c.npy'), ('sequence_3a_644_6_fv1c.npy', 'sequence_3a_644_6_label_fv1c.npy'), ('sequence_1l_387_8_fv1c.npy', 'sequence_1l_387_8_label_fv1c.npy'), ('sequence_2c_553_4_fv1c.npy', 'sequence_2c_553_4_label_fv1c.npy'), ('sequence_1d_126_7_fv1c.npy', 'sequence_1d_126_7_label_fv1c.npy'), ('sequence_3a_710_12_fv1c.npy', 'sequence_3a_710_12_label_fv1c.npy'), ('sequence_3a_567_8_fv1c.npy', 'sequence_3a_567_8_label_fv1c.npy'), ('sequence_1d_262_13_fv1c.npy', 'sequence_1d_262_13_label_fv1c.npy'), ('sequence_1d_279_30_fv1c.npy', 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('sequence_1l_365_6_fv1c.npy', 'sequence_1l_365_6_label_fv1c.npy'), ('sequence_1l_386_7_fv1c.npy', 'sequence_1l_386_7_label_fv1c.npy'), ('sequence_1c_106_7_fv1c.npy', 'sequence_1c_106_7_label_fv1c.npy'), ('sequence_3a_709_11_fv1c.npy', 'sequence_3a_709_11_label_fv1c.npy'), ('sequence_1k_334_5_fv1c.npy', 'sequence_1k_334_5_label_fv1c.npy'), ('sequence_1d_212_23_fv1c.npy', 'sequence_1d_212_23_label_fv1c.npy'), ('sequence_1b_32_3_fv1c.npy', 'sequence_1b_32_3_label_fv1c.npy'), ('sequence_3a_582_4_fv1c.npy', 'sequence_3a_582_4_label_fv1c.npy'), ('sequence_1k_352_3_fv1c.npy', 'sequence_1k_352_3_label_fv1c.npy'), ('sequence_1l_420_1_fv1c.npy', 'sequence_1l_420_1_label_fv1c.npy'), ('sequence_1d_149_20_fv1c.npy', 'sequence_1d_149_20_label_fv1c.npy'), ('sequence_1l_377_8_fv1c.npy', 'sequence_1l_377_8_label_fv1c.npy'), ('sequence_2b_483_4_fv1c.npy', 'sequence_2b_483_4_label_fv1c.npy'), ('sequence_1d_209_20_fv1c.npy', 'sequence_1d_209_20_label_fv1c.npy'), ('sequence_1d_220_1_fv1c.npy', 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('sequence_3a_634_16_fv1c.npy', 'sequence_3a_634_16_label_fv1c.npy'), ('sequence_1k_326_7_fv1c.npy', 'sequence_1k_326_7_label_fv1c.npy'), ('sequence_1k_294_5_fv1c.npy', 'sequence_1k_294_5_label_fv1c.npy'), ('sequence_1d_225_6_fv1c.npy', 'sequence_1d_225_6_label_fv1c.npy'), ('sequence_3a_630_12_fv1c.npy', 'sequence_3a_630_12_label_fv1c.npy'), ('sequence_1c_102_3_fv1c.npy', 'sequence_1c_102_3_label_fv1c.npy'), ('sequence_2b_454_5_fv1c.npy', 'sequence_2b_454_5_label_fv1c.npy'), ('sequence_1d_147_18_fv1c.npy', 'sequence_1d_147_18_label_fv1c.npy'), ('sequence_1c_90_1_fv1c.npy', 'sequence_1c_90_1_label_fv1c.npy'), ('sequence_1d_241_22_fv1c.npy', 'sequence_1d_241_22_label_fv1c.npy'), ('sequence_1b_57_8_fv1c.npy', 'sequence_1b_57_8_label_fv1c.npy'), ('sequence_3a_576_17_fv1c.npy', 'sequence_3a_576_17_label_fv1c.npy'), ('sequence_1k_331_2_fv1c.npy', 'sequence_1k_331_2_label_fv1c.npy'), ('sequence_1c_110_1_fv1c.npy', 'sequence_1c_110_1_label_fv1c.npy'), ('sequence_1k_297_8_fv1c.npy', 'sequence_1k_297_8_label_fv1c.npy'), ('sequence_3a_629_11_fv1c.npy', 'sequence_3a_629_11_label_fv1c.npy'), ('sequence_1d_124_5_fv1c.npy', 'sequence_1d_124_5_label_fv1c.npy'), ('sequence_3a_679_1_fv1c.npy', 'sequence_3a_679_1_label_fv1c.npy'), ('sequence_3a_640_2_fv1c.npy', 'sequence_3a_640_2_label_fv1c.npy'), ('sequence_1d_135_6_fv1c.npy', 'sequence_1d_135_6_label_fv1c.npy'), ('sequence_3a_713_15_fv1c.npy', 'sequence_3a_713_15_label_fv1c.npy'), ('sequence_1d_268_19_fv1c.npy', 'sequence_1d_268_19_label_fv1c.npy'), ('sequence_1c_77_8_fv1c.npy', 'sequence_1c_77_8_label_fv1c.npy'), ('sequence_1b_1_2_fv1c.npy', 'sequence_1b_1_2_label_fv1c.npy'), ('sequence_3a_599_1_fv1c.npy', 'sequence_3a_599_1_label_fv1c.npy'), ('sequence_1l_401_2_fv1c.npy', 'sequence_1l_401_2_label_fv1c.npy'), ('sequence_1k_344_5_fv1c.npy', 'sequence_1k_344_5_label_fv1c.npy'), ('sequence_1c_113_4_fv1c.npy', 'sequence_1c_113_4_label_fv1c.npy'), ('sequence_2b_461_2_fv1c.npy', 'sequence_2b_461_2_label_fv1c.npy'), ('sequence_1k_301_2_fv1c.npy', 'sequence_1k_301_2_label_fv1c.npy'), ('sequence_1d_251_2_fv1c.npy', 'sequence_1d_251_2_label_fv1c.npy'), ('sequence_1c_66_7_fv1c.npy', 'sequence_1c_66_7_label_fv1c.npy'), ('sequence_1b_30_1_fv1c.npy', 'sequence_1b_30_1_label_fv1c.npy')]
Training data read.
after shuffling: [('sequence_1b_29_10_fv1c.npy', 'sequence_1b_29_10_label_fv1c.npy'), ('sequence_2b_459_10_fv1c.npy', 'sequence_2b_459_10_label_fv1c.npy'), ('sequence_2c_528_9_fv1c.npy', 'sequence_2c_528_9_label_fv1c.npy'), ('sequence_1l_409_10_fv1c.npy', 'sequence_1l_409_10_label_fv1c.npy'), ('sequence_1b_19_10_fv1c.npy', 'sequence_1b_19_10_label_fv1c.npy'), ('sequence_1k_348_9_fv1c.npy', 'sequence_1k_348_9_label_fv1c.npy'), ('sequence_2b_488_9_fv1c.npy', 'sequence_2b_488_9_label_fv1c.npy'), ('sequence_1l_418_9_fv1c.npy', 'sequence_1l_418_9_label_fv1c.npy'), ('sequence_1d_129_10_fv1c.npy', 'sequence_1d_129_10_label_fv1c.npy'), ('sequence_3a_587_9_fv1c.npy', 'sequence_3a_587_9_label_fv1c.npy'), ('sequence_1k_358_9_fv1c.npy', 'sequence_1k_358_9_label_fv1c.npy'), ('sequence_3a_569_10_fv1c.npy', 'sequence_3a_569_10_label_fv1c.npy'), ('sequence_1b_48_9_fv1c.npy', 'sequence_1b_48_9_label_fv1c.npy'), ('sequence_2b_478_9_fv1c.npy', 'sequence_2b_478_9_label_fv1c.npy'), ('sequence_1l_438_9_fv1c.npy', 'sequence_1l_438_9_label_fv1c.npy'), ('sequence_1b_9_10_fv1c.npy', 'sequence_1b_9_10_label_fv1c.npy'), ('sequence_1l_368_9_fv1c.npy', 'sequence_1l_368_9_label_fv1c.npy'), ('sequence_2b_468_9_fv1c.npy', 'sequence_2b_468_9_label_fv1c.npy'), ('sequence_2c_518_9_fv1c.npy', 'sequence_2c_518_9_label_fv1c.npy'), ('sequence_1l_428_9_fv1c.npy', 'sequence_1l_428_9_label_fv1c.npy'), ('sequence_2c_538_9_fv1c.npy', 'sequence_2c_538_9_label_fv1c.npy'), ('sequence_2b_469_10_fv1c.npy', 'sequence_2b_469_10_label_fv1c.npy'), ('sequence_3a_668_10_fv1c.npy', 'sequence_3a_668_10_label_fv1c.npy'), ('sequence_1k_289_10_fv1c.npy', 'sequence_1k_289_10_label_fv1c.npy'), ('sequence_2c_539_10_fv1c.npy', 'sequence_2c_539_10_label_fv1c.npy'), ('sequence_3a_687_9_fv1c.npy', 'sequence_3a_687_9_label_fv1c.npy'), ('sequence_1b_58_9_fv1c.npy', 'sequence_1b_58_9_label_fv1c.npy'), ('sequence_1b_18_9_fv1c.npy', 'sequence_1b_18_9_label_fv1c.npy'), ('sequence_1l_389_10_fv1c.npy', 'sequence_1l_389_10_label_fv1c.npy'), ('sequence_2b_489_10_fv1c.npy', 'sequence_2b_489_10_label_fv1c.npy'), ('sequence_1k_319_10_fv1c.npy', 'sequence_1k_319_10_label_fv1c.npy'), ('sequence_1c_89_10_fv1c.npy', 'sequence_1c_89_10_label_fv1c.npy'), ('sequence_1k_359_10_fv1c.npy', 'sequence_1k_359_10_label_fv1c.npy'), ('sequence_1k_329_10_fv1c.npy', 'sequence_1k_329_10_label_fv1c.npy'), ('sequence_1c_68_9_fv1c.npy', 'sequence_1c_68_9_label_fv1c.npy'), ('sequence_1c_118_9_fv1c.npy', 'sequence_1c_118_9_label_fv1c.npy'), ('sequence_1k_328_9_fv1c.npy', 'sequence_1k_328_9_label_fv1c.npy'), ('sequence_2c_519_10_fv1c.npy', 'sequence_2c_519_10_label_fv1c.npy'), ('sequence_1b_59_10_fv1c.npy', 'sequence_1b_59_10_label_fv1c.npy'), ('sequence_1c_69_10_fv1c.npy', 'sequence_1c_69_10_label_fv1c.npy'), ('sequence_3a_627_9_fv1c.npy', 'sequence_3a_627_9_label_fv1c.npy'), ('sequence_2b_458_9_fv1c.npy', 'sequence_2b_458_9_label_fv1c.npy'), ('sequence_1d_169_10_fv1c.npy', 'sequence_1d_169_10_label_fv1c.npy'), ('sequence_1b_39_10_fv1c.npy', 'sequence_1b_39_10_label_fv1c.npy'), ('sequence_1l_439_10_fv1c.npy', 'sequence_1l_439_10_label_fv1c.npy'), ('sequence_3a_568_9_fv1c.npy', 'sequence_3a_568_9_label_fv1c.npy'), ('sequence_1d_228_9_fv1c.npy', 'sequence_1d_228_9_label_fv1c.npy'), ('sequence_2c_558_9_fv1c.npy', 'sequence_2c_558_9_label_fv1c.npy'), ('sequence_1c_119_10_fv1c.npy', 'sequence_1c_119_10_label_fv1c.npy'), ('sequence_2c_508_9_fv1c.npy', 'sequence_2c_508_9_label_fv1c.npy'), ('sequence_1d_138_9_fv1c.npy', 'sequence_1d_138_9_label_fv1c.npy'), ('sequence_2c_509_10_fv1c.npy', 'sequence_2c_509_10_label_fv1c.npy'), ('sequence_3a_667_9_fv1c.npy', 'sequence_3a_667_9_label_fv1c.npy'), ('sequence_3a_588_10_fv1c.npy', 'sequence_3a_588_10_label_fv1c.npy'), ('sequence_1d_168_9_fv1c.npy', 'sequence_1d_168_9_label_fv1c.npy'), ('sequence_1b_28_9_fv1c.npy', 'sequence_1b_28_9_label_fv1c.npy'), ('sequence_1k_308_9_fv1c.npy', 'sequence_1k_308_9_label_fv1c.npy'), ('sequence_3a_608_10_fv1c.npy', 'sequence_3a_608_10_label_fv1c.npy'), ('sequence_3a_628_10_fv1c.npy', 'sequence_3a_628_10_label_fv1c.npy'), ('sequence_1k_299_10_fv1c.npy', 'sequence_1k_299_10_label_fv1c.npy'), ('sequence_1c_108_9_fv1c.npy', 'sequence_1c_108_9_label_fv1c.npy'), ('sequence_1b_8_9_fv1c.npy', 'sequence_1b_8_9_label_fv1c.npy'), ('sequence_1c_98_9_fv1c.npy', 'sequence_1c_98_9_label_fv1c.npy'), ('sequence_1c_78_9_fv1c.npy', 'sequence_1c_78_9_label_fv1c.npy'), ('sequence_1d_229_10_fv1c.npy', 'sequence_1d_229_10_label_fv1c.npy'), ('sequence_2c_548_9_fv1c.npy', 'sequence_2c_548_9_label_fv1c.npy'), ('sequence_3a_648_10_fv1c.npy', 'sequence_3a_648_10_label_fv1c.npy'), ('sequence_1d_139_10_fv1c.npy', 'sequence_1d_139_10_label_fv1c.npy'), ('sequence_1l_398_9_fv1c.npy', 'sequence_1l_398_9_label_fv1c.npy'), ('sequence_3a_607_9_fv1c.npy', 'sequence_3a_607_9_label_fv1c.npy'), ('sequence_3a_688_10_fv1c.npy', 'sequence_3a_688_10_label_fv1c.npy'), ('sequence_1l_419_10_fv1c.npy', 'sequence_1l_419_10_label_fv1c.npy'), ('sequence_2c_549_10_fv1c.npy', 'sequence_2c_549_10_label_fv1c.npy'), ('sequence_1l_379_10_fv1c.npy', 'sequence_1l_379_10_label_fv1c.npy'), ('sequence_3a_707_9_fv1c.npy', 'sequence_3a_707_9_label_fv1c.npy'), ('sequence_2b_448_9_fv1c.npy', 'sequence_2b_448_9_label_fv1c.npy'), ('sequence_3a_708_10_fv1c.npy', 'sequence_3a_708_10_label_fv1c.npy'), ('sequence_1k_318_9_fv1c.npy', 'sequence_1k_318_9_label_fv1c.npy'), ('sequence_1d_198_9_fv1c.npy', 'sequence_1d_198_9_label_fv1c.npy'), ('sequence_1l_388_9_fv1c.npy', 'sequence_1l_388_9_label_fv1c.npy'), ('sequence_1k_309_10_fv1c.npy', 'sequence_1k_309_10_label_fv1c.npy'), ('sequence_2b_499_10_fv1c.npy', 'sequence_2b_499_10_label_fv1c.npy'), ('sequence_2c_559_10_fv1c.npy', 'sequence_2c_559_10_label_fv1c.npy'), ('sequence_1k_338_9_fv1c.npy', 'sequence_1k_338_9_label_fv1c.npy'), ('sequence_1k_298_9_fv1c.npy', 'sequence_1k_298_9_label_fv1c.npy'), ('sequence_1l_378_9_fv1c.npy', 'sequence_1l_378_9_label_fv1c.npy'), ('sequence_1l_429_10_fv1c.npy', 'sequence_1l_429_10_label_fv1c.npy'), ('sequence_1c_88_9_fv1c.npy', 'sequence_1c_88_9_label_fv1c.npy'), ('sequence_1d_258_9_fv1c.npy', 'sequence_1d_258_9_label_fv1c.npy'), ('sequence_1k_339_10_fv1c.npy', 'sequence_1k_339_10_label_fv1c.npy'), ('sequence_1b_38_9_fv1c.npy', 'sequence_1b_38_9_label_fv1c.npy'), ('sequence_2b_449_10_fv1c.npy', 'sequence_2b_449_10_label_fv1c.npy'), ('sequence_1k_288_9_fv1c.npy', 'sequence_1k_288_9_label_fv1c.npy'), ('sequence_1d_259_10_fv1c.npy', 'sequence_1d_259_10_label_fv1c.npy'), ('sequence_1d_199_10_fv1c.npy', 'sequence_1d_199_10_label_fv1c.npy'), ('sequence_2c_529_10_fv1c.npy', 'sequence_2c_529_10_label_fv1c.npy'), ('sequence_1c_109_10_fv1c.npy', 'sequence_1c_109_10_label_fv1c.npy'), ('sequence_1l_369_10_fv1c.npy', 'sequence_1l_369_10_label_fv1c.npy'), ('sequence_3a_647_9_fv1c.npy', 'sequence_3a_647_9_label_fv1c.npy'), ('sequence_1l_408_9_fv1c.npy', 'sequence_1l_408_9_label_fv1c.npy'), ('sequence_2b_498_9_fv1c.npy', 'sequence_2b_498_9_label_fv1c.npy'), ('sequence_1c_79_10_fv1c.npy', 'sequence_1c_79_10_label_fv1c.npy'), ('sequence_2b_479_10_fv1c.npy', 'sequence_2b_479_10_label_fv1c.npy'), ('sequence_1b_49_10_fv1c.npy', 'sequence_1b_49_10_label_fv1c.npy'), ('sequence_1c_99_10_fv1c.npy', 'sequence_1c_99_10_label_fv1c.npy'), ('sequence_1l_399_10_fv1c.npy', 'sequence_1l_399_10_label_fv1c.npy'), ('sequence_1k_349_10_fv1c.npy', 'sequence_1k_349_10_label_fv1c.npy'), ('sequence_1d_128_9_fv1c.npy', 'sequence_1d_128_9_label_fv1c.npy')]
Test set read.
[('sequence_1d_239_20_fv1c.npy', 'sequence_1d_239_20_label_fv1c.npy'), ('sequence_1k_327_8_fv1c.npy', 'sequence_1k_327_8_label_fv1c.npy'), ('sequence_2c_514_5_fv1c.npy', 'sequence_2c_514_5_label_fv1c.npy'), ('sequence_2c_517_8_fv1c.npy', 'sequence_2c_517_8_label_fv1c.npy'), ('sequence_1c_100_1_fv1c.npy', 'sequence_1c_100_1_label_fv1c.npy'), ('sequence_1c_65_6_fv1c.npy', 'sequence_1c_65_6_label_fv1c.npy'), ('sequence_3a_677_19_fv1c.npy', 'sequence_3a_677_19_label_fv1c.npy'), ('sequence_1b_11_2_fv1c.npy', 'sequence_1b_11_2_label_fv1c.npy'), ('sequence_1d_192_3_fv1c.npy', 'sequence_1d_192_3_label_fv1c.npy'), ('sequence_3a_601_3_fv1c.npy', 'sequence_3a_601_3_label_fv1c.npy'), ('sequence_1d_180_21_fv1c.npy', 'sequence_1d_180_21_label_fv1c.npy'), ('sequence_2c_550_1_fv1c.npy', 'sequence_2c_550_1_label_fv1c.npy'), ('sequence_1d_121_2_fv1c.npy', 'sequence_1d_121_2_label_fv1c.npy'), ('sequence_1k_306_7_fv1c.npy', 'sequence_1k_306_7_label_fv1c.npy'), ('sequence_3a_610_12_fv1c.npy', 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[('sequence_1b_29_10_fv1c.npy', 'sequence_1b_29_10_label_fv1c.npy'), ('sequence_2b_459_10_fv1c.npy', 'sequence_2b_459_10_label_fv1c.npy'), ('sequence_2c_528_9_fv1c.npy', 'sequence_2c_528_9_label_fv1c.npy'), ('sequence_1l_409_10_fv1c.npy', 'sequence_1l_409_10_label_fv1c.npy'), ('sequence_1b_19_10_fv1c.npy', 'sequence_1b_19_10_label_fv1c.npy'), ('sequence_1k_348_9_fv1c.npy', 'sequence_1k_348_9_label_fv1c.npy'), ('sequence_2b_488_9_fv1c.npy', 'sequence_2b_488_9_label_fv1c.npy'), ('sequence_1l_418_9_fv1c.npy', 'sequence_1l_418_9_label_fv1c.npy'), ('sequence_1d_129_10_fv1c.npy', 'sequence_1d_129_10_label_fv1c.npy'), ('sequence_3a_587_9_fv1c.npy', 'sequence_3a_587_9_label_fv1c.npy'), ('sequence_1k_358_9_fv1c.npy', 'sequence_1k_358_9_label_fv1c.npy'), ('sequence_3a_569_10_fv1c.npy', 'sequence_3a_569_10_label_fv1c.npy'), ('sequence_1b_48_9_fv1c.npy', 'sequence_1b_48_9_label_fv1c.npy'), ('sequence_2b_478_9_fv1c.npy', 'sequence_2b_478_9_label_fv1c.npy'), ('sequence_1l_438_9_fv1c.npy', 'sequence_1l_438_9_label_fv1c.npy'), ('sequence_1b_9_10_fv1c.npy', 'sequence_1b_9_10_label_fv1c.npy'), ('sequence_1l_368_9_fv1c.npy', 'sequence_1l_368_9_label_fv1c.npy'), ('sequence_2b_468_9_fv1c.npy', 'sequence_2b_468_9_label_fv1c.npy'), ('sequence_2c_518_9_fv1c.npy', 'sequence_2c_518_9_label_fv1c.npy'), ('sequence_1l_428_9_fv1c.npy', 'sequence_1l_428_9_label_fv1c.npy'), ('sequence_2c_538_9_fv1c.npy', 'sequence_2c_538_9_label_fv1c.npy'), ('sequence_2b_469_10_fv1c.npy', 'sequence_2b_469_10_label_fv1c.npy'), ('sequence_3a_668_10_fv1c.npy', 'sequence_3a_668_10_label_fv1c.npy'), ('sequence_1k_289_10_fv1c.npy', 'sequence_1k_289_10_label_fv1c.npy'), ('sequence_2c_539_10_fv1c.npy', 'sequence_2c_539_10_label_fv1c.npy'), ('sequence_3a_687_9_fv1c.npy', 'sequence_3a_687_9_label_fv1c.npy'), ('sequence_1b_58_9_fv1c.npy', 'sequence_1b_58_9_label_fv1c.npy'), ('sequence_1b_18_9_fv1c.npy', 'sequence_1b_18_9_label_fv1c.npy'), ('sequence_1l_389_10_fv1c.npy', 'sequence_1l_389_10_label_fv1c.npy'), ('sequence_2b_489_10_fv1c.npy', 'sequence_2b_489_10_label_fv1c.npy'), ('sequence_1k_319_10_fv1c.npy', 'sequence_1k_319_10_label_fv1c.npy'), ('sequence_1c_89_10_fv1c.npy', 'sequence_1c_89_10_label_fv1c.npy'), ('sequence_1k_359_10_fv1c.npy', 'sequence_1k_359_10_label_fv1c.npy'), ('sequence_1k_329_10_fv1c.npy', 'sequence_1k_329_10_label_fv1c.npy'), ('sequence_1c_68_9_fv1c.npy', 'sequence_1c_68_9_label_fv1c.npy'), ('sequence_1c_118_9_fv1c.npy', 'sequence_1c_118_9_label_fv1c.npy'), ('sequence_1k_328_9_fv1c.npy', 'sequence_1k_328_9_label_fv1c.npy'), ('sequence_2c_519_10_fv1c.npy', 'sequence_2c_519_10_label_fv1c.npy'), ('sequence_1b_59_10_fv1c.npy', 'sequence_1b_59_10_label_fv1c.npy'), ('sequence_1c_69_10_fv1c.npy', 'sequence_1c_69_10_label_fv1c.npy'), ('sequence_3a_627_9_fv1c.npy', 'sequence_3a_627_9_label_fv1c.npy'), ('sequence_2b_458_9_fv1c.npy', 'sequence_2b_458_9_label_fv1c.npy'), ('sequence_1d_169_10_fv1c.npy', 'sequence_1d_169_10_label_fv1c.npy'), ('sequence_1b_39_10_fv1c.npy', 'sequence_1b_39_10_label_fv1c.npy'), ('sequence_1l_439_10_fv1c.npy', 'sequence_1l_439_10_label_fv1c.npy'), ('sequence_3a_568_9_fv1c.npy', 'sequence_3a_568_9_label_fv1c.npy'), ('sequence_1d_228_9_fv1c.npy', 'sequence_1d_228_9_label_fv1c.npy'), ('sequence_2c_558_9_fv1c.npy', 'sequence_2c_558_9_label_fv1c.npy'), ('sequence_1c_119_10_fv1c.npy', 'sequence_1c_119_10_label_fv1c.npy'), ('sequence_2c_508_9_fv1c.npy', 'sequence_2c_508_9_label_fv1c.npy'), ('sequence_1d_138_9_fv1c.npy', 'sequence_1d_138_9_label_fv1c.npy'), ('sequence_2c_509_10_fv1c.npy', 'sequence_2c_509_10_label_fv1c.npy'), ('sequence_3a_667_9_fv1c.npy', 'sequence_3a_667_9_label_fv1c.npy'), ('sequence_3a_588_10_fv1c.npy', 'sequence_3a_588_10_label_fv1c.npy'), ('sequence_1d_168_9_fv1c.npy', 'sequence_1d_168_9_label_fv1c.npy'), ('sequence_1b_28_9_fv1c.npy', 'sequence_1b_28_9_label_fv1c.npy'), ('sequence_1k_308_9_fv1c.npy', 'sequence_1k_308_9_label_fv1c.npy'), ('sequence_3a_608_10_fv1c.npy', 'sequence_3a_608_10_label_fv1c.npy'), ('sequence_3a_628_10_fv1c.npy', 'sequence_3a_628_10_label_fv1c.npy'), ('sequence_1k_299_10_fv1c.npy', 'sequence_1k_299_10_label_fv1c.npy'), ('sequence_1c_108_9_fv1c.npy', 'sequence_1c_108_9_label_fv1c.npy'), ('sequence_1b_8_9_fv1c.npy', 'sequence_1b_8_9_label_fv1c.npy'), ('sequence_1c_98_9_fv1c.npy', 'sequence_1c_98_9_label_fv1c.npy'), ('sequence_1c_78_9_fv1c.npy', 'sequence_1c_78_9_label_fv1c.npy'), ('sequence_1d_229_10_fv1c.npy', 'sequence_1d_229_10_label_fv1c.npy'), ('sequence_2c_548_9_fv1c.npy', 'sequence_2c_548_9_label_fv1c.npy'), ('sequence_3a_648_10_fv1c.npy', 'sequence_3a_648_10_label_fv1c.npy'), ('sequence_1d_139_10_fv1c.npy', 'sequence_1d_139_10_label_fv1c.npy'), ('sequence_1l_398_9_fv1c.npy', 'sequence_1l_398_9_label_fv1c.npy'), ('sequence_3a_607_9_fv1c.npy', 'sequence_3a_607_9_label_fv1c.npy'), ('sequence_3a_688_10_fv1c.npy', 'sequence_3a_688_10_label_fv1c.npy'), ('sequence_1l_419_10_fv1c.npy', 'sequence_1l_419_10_label_fv1c.npy'), ('sequence_2c_549_10_fv1c.npy', 'sequence_2c_549_10_label_fv1c.npy'), ('sequence_1l_379_10_fv1c.npy', 'sequence_1l_379_10_label_fv1c.npy'), ('sequence_3a_707_9_fv1c.npy', 'sequence_3a_707_9_label_fv1c.npy'), ('sequence_2b_448_9_fv1c.npy', 'sequence_2b_448_9_label_fv1c.npy'), ('sequence_3a_708_10_fv1c.npy', 'sequence_3a_708_10_label_fv1c.npy'), ('sequence_1k_318_9_fv1c.npy', 'sequence_1k_318_9_label_fv1c.npy'), ('sequence_1d_198_9_fv1c.npy', 'sequence_1d_198_9_label_fv1c.npy'), ('sequence_1l_388_9_fv1c.npy', 'sequence_1l_388_9_label_fv1c.npy'), ('sequence_1k_309_10_fv1c.npy', 'sequence_1k_309_10_label_fv1c.npy'), ('sequence_2b_499_10_fv1c.npy', 'sequence_2b_499_10_label_fv1c.npy'), ('sequence_2c_559_10_fv1c.npy', 'sequence_2c_559_10_label_fv1c.npy'), ('sequence_1k_338_9_fv1c.npy', 'sequence_1k_338_9_label_fv1c.npy'), ('sequence_1k_298_9_fv1c.npy', 'sequence_1k_298_9_label_fv1c.npy'), ('sequence_1l_378_9_fv1c.npy', 'sequence_1l_378_9_label_fv1c.npy'), ('sequence_1l_429_10_fv1c.npy', 'sequence_1l_429_10_label_fv1c.npy'), ('sequence_1c_88_9_fv1c.npy', 'sequence_1c_88_9_label_fv1c.npy'), ('sequence_1d_258_9_fv1c.npy', 'sequence_1d_258_9_label_fv1c.npy'), ('sequence_1k_339_10_fv1c.npy', 'sequence_1k_339_10_label_fv1c.npy'), ('sequence_1b_38_9_fv1c.npy', 'sequence_1b_38_9_label_fv1c.npy'), ('sequence_2b_449_10_fv1c.npy', 'sequence_2b_449_10_label_fv1c.npy'), ('sequence_1k_288_9_fv1c.npy', 'sequence_1k_288_9_label_fv1c.npy'), ('sequence_1d_259_10_fv1c.npy', 'sequence_1d_259_10_label_fv1c.npy'), ('sequence_1d_199_10_fv1c.npy', 'sequence_1d_199_10_label_fv1c.npy'), ('sequence_2c_529_10_fv1c.npy', 'sequence_2c_529_10_label_fv1c.npy'), ('sequence_1c_109_10_fv1c.npy', 'sequence_1c_109_10_label_fv1c.npy'), ('sequence_1l_369_10_fv1c.npy', 'sequence_1l_369_10_label_fv1c.npy'), ('sequence_3a_647_9_fv1c.npy', 'sequence_3a_647_9_label_fv1c.npy'), ('sequence_1l_408_9_fv1c.npy', 'sequence_1l_408_9_label_fv1c.npy'), ('sequence_2b_498_9_fv1c.npy', 'sequence_2b_498_9_label_fv1c.npy'), ('sequence_1c_79_10_fv1c.npy', 'sequence_1c_79_10_label_fv1c.npy'), ('sequence_2b_479_10_fv1c.npy', 'sequence_2b_479_10_label_fv1c.npy'), ('sequence_1b_49_10_fv1c.npy', 'sequence_1b_49_10_label_fv1c.npy'), ('sequence_1c_99_10_fv1c.npy', 'sequence_1c_99_10_label_fv1c.npy'), ('sequence_1l_399_10_fv1c.npy', 'sequence_1l_399_10_label_fv1c.npy'), ('sequence_1k_349_10_fv1c.npy', 'sequence_1k_349_10_label_fv1c.npy'), ('sequence_1d_128_9_fv1c.npy', 'sequence_1d_128_9_label_fv1c.npy')]

In [2]:

    

score_per_ic_df = pd.read_csv(analysis_path+'sn_ic_grouped_by_ic.csv',index_col=0)
score_per_loadseq_df = pd.read_csv(analysis_path+'srls_grouped_by_ls.csv',index_col=0)
print(score_per_ic_df.index)
print(score_per_loadseq_df.index)


    

    




Index([u'[ 0.410625  1.335625  1.515625  2.130625]',
       u'[ 0.420625  1.345625  1.425625  2.215625]',
       u'[ 0.490625  1.365625  1.415625  2.253125]',
       u'[ 0.610625  1.335625  1.515625  2.130625]',
       u'[ 0.620625  1.335625  1.515625  2.130625]',
       u'[ 0.725   1.475   1.525   2.2375]'],
      dtype='object', name=u'init_cond')
Index([u'[  1.028  10.28   10.28    0.75    0.75   10.28   10.28    1.028]',
       u'[  1.028  11.28   11.28    1.      1.     10.28   10.28    1.028]',
       u'[  1.028  12.28   12.28    1.028   1.028  17.28   17.28    1.028]',
       u'[  1.028  13.28   13.28    1.25    1.25   13.28   13.28    1.028]',
       u'[  1.028  14.28   14.28    0.75    0.75   16.28   16.28    1.028]',
       u'[  1.028  15.28   15.28    1.75    1.75   10.28   10.28    1.028]',
       u'[  1.428  12.28   12.28    1.428   1.428  16.28   16.28    1.428   1.428\n  14.28   14.28    0.75    0.75   14.28   14.28    1.428   1.428  14.28\n  14.28    0.75    0.75   16.28   16.28    1.428]',
       u'[  1.428  12.28   12.28    1.428   1.428  16.28   16.28    1.428   1.428\n  14.28   14.28    1.428   1.428  14.28   14.28    1.428   1.428  12.28\n  12.28    1.428   1.428  16.28   16.28    1.428]',
       u'[  1.428  12.28   12.28    1.428   1.428  16.28   16.28    1.428]',
       u'[  1.428  14.28   14.28    0.5     0.5    16.28   16.28    1.428   1.428\n  12.28   12.28    1.428   1.428  16.28   16.28    1.428   1.428  14.28\n  14.28    1.428   1.428  14.28   14.28    1.428]',
       u'[  1.428  14.28   14.28    0.5     0.5    16.28   16.28    1.428   1.428\n  14.28   14.28    1.      1.     14.28   14.28    1.428   1.428  14.28\n  14.28    0.5     0.5    16.28   16.28    1.428]',
       u'[  1.428  14.28   14.28    0.75    0.75   14.28   14.28    1.428   1.428\n  14.28   14.28    1.25    1.25   14.28   14.28    1.428   1.428  14.28\n  14.28    1.75    1.75   14.28   14.28    1.428]',
       u'[  1.428  14.28   14.28    0.75    0.75   14.28   14.28    1.428]',
       u'[  1.428  14.28   14.28    0.75    0.75   16.28   16.28    1.428   1.428\n  12.28   12.28    1.428   1.428  16.28   16.28    1.428   1.428  14.28\n  14.28    0.75    0.75   16.28   16.28    1.428]',
       u'[  1.428  14.28   14.28    0.75    0.75   16.28   16.28    1.428   1.428\n  14.28   14.28    1.75    1.75   14.28   14.28    1.428   1.428  14.28\n  14.28    0.75    0.75   14.28   14.28    1.428]',
       u'[  1.428  14.28   14.28    0.75    0.75   16.28   16.28    1.428]',
       u'[  1.428  14.28   14.28    1.      1.     14.28   14.28    1.428   1.428\n   9.5     9.5     7.5     7.5    14.28   14.28    1.428   1.428  12.5\n  12.5     7.5     7.5     8.5     8.5     1.428]',
       u'[  1.428  14.28   14.28    1.      1.     14.28   14.28    1.428   1.428\n  14.28   14.28    1.25    1.25   14.28   14.28    1.428   1.428  14.28\n  14.28    1.      1.     14.28   14.28    1.428]',
       u'[  1.428  14.28   14.28    1.      1.     14.28   14.28    1.428]',
       u'[  1.428  14.28   14.28    1.25    1.25   14.28   14.28    1.428   1.428\n  14.28   14.28    1.      1.     14.28   14.28    1.428   1.428  13.5\n  13.5     1.      1.     11.5    11.5     1.428]',
       u'[  1.428  14.28   14.28    1.25    1.25   14.28   14.28    1.428   1.428\n  14.28   14.28    1.75    1.75   14.28   14.28    1.428   1.428  14.28\n  14.28    1.25    1.25   14.28   14.28    1.428]',
       u'[  1.428  14.28   14.28    1.25    1.25   14.28   14.28    1.428]',
       u'[  1.428  14.28   14.28    1.428   1.428  14.28   14.28    1.428   1.428\n  14.28   14.28    0.5     0.5    16.28   16.28    1.428   1.428  14.28\n  14.28    1.428   1.428  14.28   14.28    1.428]',
       u'[  1.428  14.28   14.28    1.428   1.428  14.28   14.28    1.428   1.428\n  14.28   14.28    0.75    0.75   16.28   16.28    1.428   1.428  12.28\n  12.28    1.428   1.428  16.28   16.28    1.428]',
       u'[  1.428  14.28   14.28    1.75    1.75   14.28   14.28    1.428   1.428\n  14.28   14.28    0.75    0.75   14.28   14.28    1.428   1.428  14.28\n  14.28    1.75    1.75   14.28   14.28    1.428]',
       u'[  1.428  14.28   14.28    1.75    1.75   14.28   14.28    1.428   1.428\n  14.28   14.28    1.      1.     14.28   14.28    1.428   1.428  14.28\n  14.28    1.25    1.25   14.28   14.28    1.428]',
       u'[  1.428  14.28   14.28    1.75    1.75   14.28   14.28    1.428]'],
      dtype='object', name=u'loadseq')

In [10]:

    

seqname_initcond_df = pd.read_csv(analysis_path + 'combined_seqname_initcond_dict.csv')
seqname_loadseq_df = pd.read_csv(analysis_path + 'seqname_score_loadseq_TEST.csv')
#loadseq_initcond_pair_df = pd.merge(seqname_initcond_df,seqname_loadseq_df,how='inner',left_index=True,right_index=True)
loadseq_initcond_pair_df = pd.merge(seqname_initcond_df,seqname_loadseq_df,how='inner',left_on='seq_name',right_on='seq_name')
#ls_ic_pairs = list(zip(loadseq_initcond_pair_df.loc[:,'loadseq'],loadseq_initcond_pair_df.loc[:,'init_cond']))
loadseq_initcond_pair_df.reset_index()

#set the index to be loadseq and init cond. 
loadseq_initcond_pair_df.set_index(keys=['loadseq','init_cond'],inplace=True)
print('index',loadseq_initcond_pair_df.index)
print('head 1',loadseq_initcond_pair_df.head(1))


    

    




('index', MultiIndex(levels=[[u'[  1.028  10.28   10.28    0.75    0.75   10.28   10.28    1.028]', u'[  1.028  11.28   11.28    1.      1.     10.28   10.28    1.028]', u'[  1.028  12.28   12.28    1.028   1.028  17.28   17.28    1.028]', u'[  1.028  13.28   13.28    1.25    1.25   13.28   13.28    1.028]', u'[  1.028  14.28   14.28    0.75    0.75   16.28   16.28    1.028]', u'[  1.028  15.28   15.28    1.75    1.75   10.28   10.28    1.028]', u'[  1.428  12.28   12.28    1.428   1.428  16.28   16.28    1.428   1.428\n  14.28   14.28    0.75    0.75   14.28   14.28    1.428   1.428  14.28\n  14.28    0.75    0.75   16.28   16.28    1.428]', u'[  1.428  12.28   12.28    1.428   1.428  16.28   16.28    1.428   1.428\n  14.28   14.28    1.428   1.428  14.28   14.28    1.428   1.428  12.28\n  12.28    1.428   1.428  16.28   16.28    1.428]', u'[  1.428  12.28   12.28    1.428   1.428  16.28   16.28    1.428]', u'[  1.428  14.28   14.28    0.5     0.5    16.28   16.28    1.428   1.428\n  12.28   12.28    1.428   1.428  16.28   16.28    1.428   1.428  14.28\n  14.28    1.428   1.428  14.28   14.28    1.428]', u'[  1.428  14.28   14.28    0.5     0.5    16.28   16.28    1.428   1.428\n  14.28   14.28    1.      1.     14.28   14.28    1.428   1.428  14.28\n  14.28    0.5     0.5    16.28   16.28    1.428]', u'[  1.428  14.28   14.28    0.75    0.75   14.28   14.28    1.428   1.428\n  14.28   14.28    1.25    1.25   14.28   14.28    1.428   1.428  14.28\n  14.28    1.75    1.75   14.28   14.28    1.428]', u'[  1.428  14.28   14.28    0.75    0.75   14.28   14.28    1.428]', u'[  1.428  14.28   14.28    0.75    0.75   16.28   16.28    1.428   1.428\n  12.28   12.28    1.428   1.428  16.28   16.28    1.428   1.428  14.28\n  14.28    0.75    0.75   16.28   16.28    1.428]', u'[  1.428  14.28   14.28    0.75    0.75   16.28   16.28    1.428   1.428\n  14.28   14.28    1.75    1.75   14.28   14.28    1.428   1.428  14.28\n  14.28    0.75    0.75   14.28   14.28    1.428]', u'[  1.428  14.28   14.28    0.75    0.75   16.28   16.28    1.428]', u'[  1.428  14.28   14.28    1.      1.     14.28   14.28    1.428   1.428\n   9.5     9.5     7.5     7.5    14.28   14.28    1.428   1.428  12.5\n  12.5     7.5     7.5     8.5     8.5     1.428]', u'[  1.428  14.28   14.28    1.      1.     14.28   14.28    1.428   1.428\n  14.28   14.28    1.25    1.25   14.28   14.28    1.428   1.428  14.28\n  14.28    1.      1.     14.28   14.28    1.428]', u'[  1.428  14.28   14.28    1.      1.     14.28   14.28    1.428]', u'[  1.428  14.28   14.28    1.25    1.25   14.28   14.28    1.428   1.428\n  14.28   14.28    1.      1.     14.28   14.28    1.428   1.428  13.5\n  13.5     1.      1.     11.5    11.5     1.428]', u'[  1.428  14.28   14.28    1.25    1.25   14.28   14.28    1.428   1.428\n  14.28   14.28    1.75    1.75   14.28   14.28    1.428   1.428  14.28\n  14.28    1.25    1.25   14.28   14.28    1.428]', u'[  1.428  14.28   14.28    1.25    1.25   14.28   14.28    1.428]', u'[  1.428  14.28   14.28    1.428   1.428  14.28   14.28    1.428   1.428\n  14.28   14.28    0.5     0.5    16.28   16.28    1.428   1.428  14.28\n  14.28    1.428   1.428  14.28   14.28    1.428]', u'[  1.428  14.28   14.28    1.428   1.428  14.28   14.28    1.428   1.428\n  14.28   14.28    0.75    0.75   16.28   16.28    1.428   1.428  12.28\n  12.28    1.428   1.428  16.28   16.28    1.428]', u'[  1.428  14.28   14.28    1.75    1.75   14.28   14.28    1.428   1.428\n  14.28   14.28    0.75    0.75   14.28   14.28    1.428   1.428  14.28\n  14.28    1.75    1.75   14.28   14.28    1.428]', u'[  1.428  14.28   14.28    1.75    1.75   14.28   14.28    1.428   1.428\n  14.28   14.28    1.      1.     14.28   14.28    1.428   1.428  14.28\n  14.28    1.25    1.25   14.28   14.28    1.428]', u'[  1.428  14.28   14.28    1.75    1.75   14.28   14.28    1.428]'], [u'[ 0.410625  1.335625  1.515625  2.130625]', u'[ 0.420625  1.345625  1.425625  2.215625]', u'[ 0.490625  1.365625  1.415625  2.253125]', u'[ 0.610625  1.335625  1.515625  2.130625]', u'[ 0.620625  1.335625  1.515625  2.130625]', u'[ 0.725   1.475   1.525   2.2375]']],
           labels=[[26, 11, 9, 18, 4, 14, 3, 21, 15, 20, 8, 10, 13, 13, 25, 21, 3, 15, 7, 21, 12, 13, 20, 8, 0, 26, 15, 26, 8, 5, 12, 17, 6, 20, 2, 23, 21, 22, 7, 1, 7, 21, 8, 23, 21, 12, 15, 26, 25, 18, 24, 12, 15, 26, 11, 16, 16, 12, 18, 26, 14, 1, 9, 11, 15, 22, 15, 21, 12, 12, 19, 12, 22, 17, 10, 24, 18, 16, 22, 0, 18, 10, 26, 8, 25, 17, 8, 15, 26, 19, 12, 7, 8, 4, 10, 13, 6, 18, 2, 19, 18, 24, 8, 5, 21, 12, 12, 18], [5, 3, 0, 2, 2, 0, 2, 5, 5, 0, 2, 0, 4, 4, 0, 1, 2, 2, 0, 2, 2, 0, 4, 2, 2, 1, 2, 1, 1, 2, 0, 4, 0, 0, 2, 0, 2, 4, 4, 2, 0, 2, 2, 0, 1, 2, 1, 5, 3, 2, 0, 2, 5, 2, 0, 0, 0, 5, 0, 2, 0, 2, 0, 0, 2, 0, 1, 5, 4, 1, 0, 1, 0, 0, 4, 0, 0, 3, 4, 2, 1, 0, 2, 5, 0, 0, 1, 2, 2, 0, 2, 4, 2, 2, 4, 0, 0, 2, 2, 3, 2, 4, 5, 2, 2, 5, 0, 1]],
           names=[u'loadseq', u'init_cond']))
('head 1',                                                                                                     seq_name  \
loadseq                                            init_cond                                                   
[  1.428  14.28   14.28    1.75    1.75   14.28... [ 0.725   1.475   1.525   2.2375]  sequence_1d_168_9_fv1c   

                                                                                       mse_avg  \
loadseq                                            init_cond                                     
[  1.428  14.28   14.28    1.75    1.75   14.28... [ 0.725   1.475   1.525   2.2375]  0.093251   

                                                                                       mse_med  \
loadseq                                            init_cond                                     
[  1.428  14.28   14.28    1.75    1.75   14.28... [ 0.725   1.475   1.525   2.2375]  0.013695   

                                                                                      mse_stdev  \
loadseq                                            init_cond                                      
[  1.428  14.28   14.28    1.75    1.75   14.28... [ 0.725   1.475   1.525   2.2375]   0.129213   

                                                                                       mae_avg  \
loadseq                                            init_cond                                     
[  1.428  14.28   14.28    1.75    1.75   14.28... [ 0.725   1.475   1.525   2.2375]  0.141744   

                                                                                       mae_med  \
loadseq                                            init_cond                                     
[  1.428  14.28   14.28    1.75    1.75   14.28... [ 0.725   1.475   1.525   2.2375]  0.012286   

                                                                                      mae_stdev  \
loadseq                                            init_cond                                      
[  1.428  14.28   14.28    1.75    1.75   14.28... [ 0.725   1.475   1.525   2.2375]   0.187901   

                                                                                      mae_normd_dev  \
loadseq                                            init_cond                                          
[  1.428  14.28   14.28    1.75    1.75   14.28... [ 0.725   1.475   1.525   2.2375]            NaN   

                                                                                        mape_avg  \
loadseq                                            init_cond                                       
[  1.428  14.28   14.28    1.75    1.75   14.28... [ 0.725   1.475   1.525   2.2375]  483.399914   

                                                                                        mape_med  \
loadseq                                            init_cond                                       
[  1.428  14.28   14.28    1.75    1.75   14.28... [ 0.725   1.475   1.525   2.2375]  556.930833   

                                                                                      mape_stdev  \
loadseq                                            init_cond                                       
[  1.428  14.28   14.28    1.75    1.75   14.28... [ 0.725   1.475   1.525   2.2375]  174.561483   

                                                                                      msle_avg  \
loadseq                                            init_cond                                     
[  1.428  14.28   14.28    1.75    1.75   14.28... [ 0.725   1.475   1.525   2.2375]  0.063196   

                                                                                      msle_med  \
loadseq                                            init_cond                                     
[  1.428  14.28   14.28    1.75    1.75   14.28... [ 0.725   1.475   1.525   2.2375]  0.058232   

                                                                                      msle_stdev  \
loadseq                                            init_cond                                       
[  1.428  14.28   14.28    1.75    1.75   14.28... [ 0.725   1.475   1.525   2.2375]    0.009147   

                                                                                      loadseq_id  
loadseq                                            init_cond                                      
[  1.428  14.28   14.28    1.75    1.75   14.28... [ 0.725   1.475   1.525   2.2375]           0  )

In [11]:

    

# loadseq_initcond_pair_df.to_csv(analysis_path + 'loadseq_initcond_pairs.csv')
aggregated_df = pd.DataFrame(loadseq_initcond_pair_df.groupby(by=['loadseq','init_cond']).mean())


    

In [13]:

    

print("aggregated df's head", aggregated_df.head(1))
aggregated_df.to_csv(analysis_path + 'results_per_loadseq_ic_pair.csv')
#grouped = loadseq_initcond_pair_df.groupby(['loadseq','init_cond']).count()
#print("groupby as a list",list(grouped))
# pd.DataFrame(grouped).to_csv(analysis_path+'loadseq_initcond_pairs_count.csv')


    

    




("aggregated df's head",                                                                                                mse_avg  \
loadseq                                            init_cond                                             
[  1.028  10.28   10.28    0.75    0.75   10.28... [ 0.490625  1.365625  1.415625  2.253125]  0.085509   

                                                                                              mse_med  \
loadseq                                            init_cond                                            
[  1.028  10.28   10.28    0.75    0.75   10.28... [ 0.490625  1.365625  1.415625  2.253125]  0.01075   

                                                                                              mse_stdev  \
loadseq                                            init_cond                                              
[  1.028  10.28   10.28    0.75    0.75   10.28... [ 0.490625  1.365625  1.415625  2.253125]   0.119585   

                                                                                               mae_avg  \
loadseq                                            init_cond                                             
[  1.028  10.28   10.28    0.75    0.75   10.28... [ 0.490625  1.365625  1.415625  2.253125]  0.136211   

                                                                                               mae_med  \
loadseq                                            init_cond                                             
[  1.028  10.28   10.28    0.75    0.75   10.28... [ 0.490625  1.365625  1.415625  2.253125]  0.011639   

                                                                                              mae_stdev  \
loadseq                                            init_cond                                              
[  1.028  10.28   10.28    0.75    0.75   10.28... [ 0.490625  1.365625  1.415625  2.253125]   0.180903   

                                                                                              mae_normd_dev  \
loadseq                                            init_cond                                                  
[  1.028  10.28   10.28    0.75    0.75   10.28... [ 0.490625  1.365625  1.415625  2.253125]            NaN   

                                                                                                mape_avg  \
loadseq                                            init_cond                                               
[  1.028  10.28   10.28    0.75    0.75   10.28... [ 0.490625  1.365625  1.415625  2.253125]  133.474029   

                                                                                                mape_med  \
loadseq                                            init_cond                                               
[  1.028  10.28   10.28    0.75    0.75   10.28... [ 0.490625  1.365625  1.415625  2.253125]  142.569599   

                                                                                              mape_stdev  \
loadseq                                            init_cond                                               
[  1.028  10.28   10.28    0.75    0.75   10.28... [ 0.490625  1.365625  1.415625  2.253125]   43.042738   

                                                                                              msle_avg  \
loadseq                                            init_cond                                             
[  1.028  10.28   10.28    0.75    0.75   10.28... [ 0.490625  1.365625  1.415625  2.253125]  0.048845   

                                                                                              msle_med  \
loadseq                                            init_cond                                             
[  1.028  10.28   10.28    0.75    0.75   10.28... [ 0.490625  1.365625  1.415625  2.253125]  0.041371   

                                                                                              msle_stdev  \
loadseq                                            init_cond                                               
[  1.028  10.28   10.28    0.75    0.75   10.28... [ 0.490625  1.365625  1.415625  2.253125]    0.016196   

                                                                                              loadseq_id  
loadseq                                            init_cond                                              
[  1.028  10.28   10.28    0.75    0.75   10.28... [ 0.490625  1.365625  1.415625  2.253125]           7  )

In [ ]: