Use this directory to train machine learning models based on folders of files.
To get started, you just need to make at least 2 folders containing the same type of file (e.g. audio .WAV files). Ideally, these folders will have the same number of classes; otherwise, the classes will automatically balance to the lower number of classes during model training.
Now you need to run model.py:
cd allie/training
python3 model.py
The resulting output in the terminal will be something like:
AAA lllllll lllllll iiii
A:::A l:::::l l:::::l i::::i
A:::::A l:::::l l:::::l iiii
A:::::::A l:::::l l:::::l
A:::::::::A l::::l l::::l iiiiiii eeeeeeeeeeee
A:::::A:::::A l::::l l::::l i:::::i ee::::::::::::ee
A:::::A A:::::A l::::l l::::l i::::i e::::::eeeee:::::ee
A:::::A A:::::A l::::l l::::l i::::i e::::::e e:::::e
A:::::A A:::::A l::::l l::::l i::::i e:::::::eeeee::::::e
A:::::AAAAAAAAA:::::A l::::l l::::l i::::i e:::::::::::::::::e
A:::::::::::::::::::::A l::::l l::::l i::::i e::::::eeeeeeeeeee
A:::::AAAAAAAAAAAAA:::::A l::::l l::::l i::::i e:::::::e
A:::::A A:::::A l::::::ll::::::li::::::ie::::::::e
A:::::A A:::::A l::::::ll::::::li::::::i e::::::::eeeeeeee
A:::::A A:::::A l::::::ll::::::li::::::i ee:::::::::::::e
AAAAAAA AAAAAAAlllllllllllllllliiiiiiii eeeeeeeeeeeeee
is this a classification (c) or regression (r) problem?
c
what problem are you solving? (1-audio, 2-text, 3-image, 4-video, 5-csv)
1
OK cool, we got you modeling audio files
how many classes would you like to model? (2 available)
2
these are the available classes:
['females', 'males']
what is class #1
males
what is class #2
females
what is the 1-word common name for the problem you are working on? (e.g. gender for male/female classification)
gender
-----------------------------------
LOADING MODULES
-----------------------------------
Requirement already satisfied: scikit-learn==0.22.2.post1 in /usr/local/lib/python3.7/site-packages (0.22.2.post1)
Requirement already satisfied: numpy>=1.11.0 in /usr/local/lib/python3.7/site-packages (from scikit-learn==0.22.2.post1) (1.18.4)
Requirement already satisfied: scipy>=0.17.0 in /usr/local/lib/python3.7/site-packages (from scikit-learn==0.22.2.post1) (1.4.1)
Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/site-packages (from scikit-learn==0.22.2.post1) (0.15.1)
-----------------------------------
______ _____ ___ _____ _ _______ _____ ___________ _ _ _____
| ___| ___|/ _ \_ _| | | | ___ \_ _|___ /_ _| \ | | __ \
| |_ | |__ / /_\ \| | | | | | |_/ / | | / / | | | \| | | \/
| _| | __|| _ || | | | | | / | | / / | | | . ` | | __
| | | |___| | | || | | |_| | |\ \ _| |_./ /____| |_| |\ | |_\ \
\_| \____/\_| |_/\_/ \___/\_| \_|\___/\_____/\___/\_| \_/\____/
______ ___ _____ ___
| _ \/ _ \_ _/ _ \
| | | / /_\ \| |/ /_\ \
| | | | _ || || _ |
| |/ /| | | || || | | |
|___/ \_| |_/\_/\_| |_/
-----------------------------------
-----------------------------------
FEATURIZING MALES
-----------------------------------
males: 100%|█████████████████████████████████| 204/204 [00:00<00:00, 432.04it/s]
-----------------------------------
FEATURIZING FEMALES
-----------------------------------
females: 100%|███████████████████████████████| 204/204 [00:00<00:00, 792.07it/s]
-----------------------------------
_____ ______ _____ ___ _____ _____ _ _ _____
/ __ \| ___ \ ___|/ _ \_ _|_ _| \ | | __ \
| / \/| |_/ / |__ / /_\ \| | | | | \| | | \/
| | | /| __|| _ || | | | | . ` | | __
| \__/\| |\ \| |___| | | || | _| |_| |\ | |_\ \
\____/\_| \_\____/\_| |_/\_/ \___/\_| \_/\____/
___________ ___ _____ _ _ _____ _ _ _____ ______ ___ _____ ___
|_ _| ___ \/ _ \|_ _| \ | |_ _| \ | | __ \ | _ \/ _ \_ _/ _ \
| | | |_/ / /_\ \ | | | \| | | | | \| | | \/ | | | / /_\ \| |/ /_\ \
| | | /| _ | | | | . ` | | | | . ` | | __ | | | | _ || || _ |
| | | |\ \| | | |_| |_| |\ |_| |_| |\ | |_\ \ | |/ /| | | || || | | |
\_/ \_| \_\_| |_/\___/\_| \_/\___/\_| \_/\____/ |___/ \_| |_/\_/\_| |_/
-----------------------------------
-----------------------------------
REMOVING OUTLIERS
-----------------------------------
<class 'list'>
<class 'int'>
193
11
(204, 187)
(204,)
(193, 187)
(193,)
males greater than minlength (94) by 5, equalizing...
males greater than minlength (94) by 4, equalizing...
males greater than minlength (94) by 3, equalizing...
males greater than minlength (94) by 2, equalizing...
males greater than minlength (94) by 1, equalizing...
males greater than minlength (94) by 0, equalizing...
gender_ALL.CSV
gender_TRAIN.CSV
gender_TEST.CSV
----------------------------------
___________ ___ _ _ ___________ ______________ ________ _ _ _____
|_ _| ___ \/ _ \ | \ | |/ ___| ___| _ | ___ \ \/ |_ _| \ | | __ \
| | | |_/ / /_\ \| \| |\ `--.| |_ | | | | |_/ / . . | | | | \| | | \/
| | | /| _ || . ` | `--. \ _| | | | | /| |\/| | | | | . ` | | __
| | | |\ \| | | || |\ |/\__/ / | \ \_/ / |\ \| | | |_| |_| |\ | |_\ \
\_/ \_| \_\_| |_/\_| \_/\____/\_| \___/\_| \_\_| |_/\___/\_| \_/\____/
______ ___ _____ ___
| _ \/ _ \_ _/ _ \
| | | / /_\ \| |/ /_\ \
| | | | _ || || _ |
| |/ /| | | || || | | |
|___/ \_| |_/\_/\_| |_/
----------------------------------
Requirement already satisfied: scikit-learn==0.22.2.post1 in /usr/local/lib/python3.7/site-packages (0.22.2.post1)
Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/site-packages (from scikit-learn==0.22.2.post1) (0.15.1)
Requirement already satisfied: scipy>=0.17.0 in /usr/local/lib/python3.7/site-packages (from scikit-learn==0.22.2.post1) (1.4.1)
Requirement already satisfied: numpy>=1.11.0 in /usr/local/lib/python3.7/site-packages (from scikit-learn==0.22.2.post1) (1.18.4)
making transformer...
python3 transform.py audio c gender males females
Requirement already satisfied: scikit-learn==0.22.2.post1 in /usr/local/lib/python3.7/site-packages (0.22.2.post1)
Requirement already satisfied: scipy>=0.17.0 in /usr/local/lib/python3.7/site-packages (from scikit-learn==0.22.2.post1) (1.4.1)
Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/site-packages (from scikit-learn==0.22.2.post1) (0.15.1)
Requirement already satisfied: numpy>=1.11.0 in /usr/local/lib/python3.7/site-packages (from scikit-learn==0.22.2.post1) (1.18.4)
/Users/jim/Desktop/allie
True
False
True
['standard_scaler']
['pca']
['rfe']
['males']
['males', 'females']
----------LOADING MALES----------
100%|███████████████████████████████████████| 102/102 [00:00<00:00, 3633.53it/s]
----------LOADING FEMALES----------
100%|███████████████████████████████████████| 102/102 [00:00<00:00, 3253.67it/s]
[29.0, 115.48275862068965, 61.08593194873256, 222.0, 8.0, 115.0, 135.99917763157896, 2.3062946170604843, 2.4523724313147683, 14.511204587940831, 0.0, 1.5839709225558054, 1.0, 0.0, 1.0, 1.0, 1.0, 0.726785565165924, 0.07679976746065657, 0.8995575540236156, 0.6637538362298933, 0.6773691100707808, 0.5192872926710033, 0.14462205416039903, 0.8356997271918719, 0.39185128881625986, 0.43825294226507827, 0.47576864823687653, 0.15489798102781285, 0.8172667015988133, 0.33745619224839324, 0.3880062539044845, 0.5039810096886804, 0.14210116297622033, 0.8222246027925175, 0.3891757588007075, 0.41656986998926515, 0.5373808945288242, 0.13048669284410083, 0.8278757499272521, 0.42749020156019385, 0.4603386494395968, 0.576968162842806, 0.12481555452009631, 0.8326178822159916, 0.4151756068434901, 0.5352195483012459, 0.5831458034458475, 0.12993864932034976, 0.8408276288260831, 0.4086771989157548, 0.5442120810410158, 0.5991899975086484, 0.12087077223741394, 0.8512938716671109, 0.4582549119597623, 0.548583257282575, 0.6043430612098373, 0.0967011928572277, 0.833318501846635, 0.5289767977628144, 0.5386694936622187, 0.5901015163274542, 0.09036115342542611, 0.8132908481041344, 0.5183061555498681, 0.5348989197039723, 0.5577690938261909, 0.10440904667022494, 0.8051028496830286, 0.47654504571917944, 0.48986432314249784, 0.5222194992298572, 0.12036460062379664, 0.7973796198667512, 0.4203428200207039, 0.4509165396076157, -324.9346584335899, 63.40977505468484, -239.1113566990693, -536.0134915419532, -312.1029273499763, 158.69053618273082, 35.26991926915998, 224.33444977809518, 25.32709426551922, 164.09725638069276, -41.76686834014897, 36.193666229738035, 46.89516424897592, -116.64411005852219, -48.46812935048629, 40.93271834758351, 30.292873365157128, 110.30488966414437, -34.8296992058053, 40.54577852540313, -13.68074476738829, 23.578831857611142, 44.5288579949288, -81.96856948352185, -13.20824575924119, 28.01017666759282, 19.911510776447017, 79.48989729266256, -30.98446467042396, 27.506651161152135, -26.204186150756332, 16.325928650509297, 17.379122853402333, -64.23824041845967, -27.70833256772887, 14.638824890367118, 14.030449436317777, 49.746826625863726, -24.54064068297873, 12.937758655225592, 1.8907564192378423, 12.717800756091705, 32.81143480306558, -34.17480821652823, 3.017798387908008, -9.890548990017422, 11.275154613335049, 16.431256434502732, -41.48821773570883, -10.025098347722025, -2.238522066589343, 11.50921922011025, 25.053143314110734, -36.57309603680529, -2.0110753582118464, -11.28338110558961, 10.092676771445209, 12.359297810934656, -45.39044308667263, -9.744274595029339, 6.634597233918086, 8.23910310866827, 31.12846300160725, -11.374600658849563, 6.9929843274651455, -9.01995244948718e-05, 4.4746520830831e-05, -9.463474948151087e-06, -0.00017975655569362465, -9.088812250730835e-05, 0.7226232345166703, 0.3516279383632571, 1.4144159675282353, 0.082382838783397, 0.7190915579267225, 1451.143003087302, 642.4123068137746, 4547.085433482971, 395.8091024437681, 1324.495426192924, 1453.4773788957211, 426.3531685791114, 2669.3033744664745, 747.1557882330682, 1339.431286902565, 17.70035758898037, 4.253697620372516, 33.22776254607448, 8.885816697236287, 17.70216728565277, 0.00011313906725263223, 0.00018414952501188964, 0.001124512287788093, 5.7439488045929465e-06, 4.929980423185043e-05, 2670.3094482421875, 1335.5639439562065, 6836.7919921875, 355.2978515625, 2282.51953125, 0.07254682268415179, 0.04210112258843493, 0.27783203125, 0.01513671875, 0.062255859375, 0.028742285445332527, 0.011032973416149616, 0.05006047338247299, 0.005435430910438299, 0.029380839318037033]
['onset_length', 'onset_detect_mean', 'onset_detect_std', 'onset_detect_maxv', 'onset_detect_minv', 'onset_detect_median', 'tempo', 'onset_strength_mean', 'onset_strength_std', 'onset_strength_maxv', 'onset_strength_minv', 'onset_strength_median', 'rhythm_0_mean', 'rhythm_0_std', 'rhythm_0_maxv', 'rhythm_0_minv', 'rhythm_0_median', 'rhythm_1_mean', 'rhythm_1_std', 'rhythm_1_maxv', 'rhythm_1_minv', 'rhythm_1_median', 'rhythm_2_mean', 'rhythm_2_std', 'rhythm_2_maxv', 'rhythm_2_minv', 'rhythm_2_median', 'rhythm_3_mean', 'rhythm_3_std', 'rhythm_3_maxv', 'rhythm_3_minv', 'rhythm_3_median', 'rhythm_4_mean', 'rhythm_4_std', 'rhythm_4_maxv', 'rhythm_4_minv', 'rhythm_4_median', 'rhythm_5_mean', 'rhythm_5_std', 'rhythm_5_maxv', 'rhythm_5_minv', 'rhythm_5_median', 'rhythm_6_mean', 'rhythm_6_std', 'rhythm_6_maxv', 'rhythm_6_minv', 'rhythm_6_median', 'rhythm_7_mean', 'rhythm_7_std', 'rhythm_7_maxv', 'rhythm_7_minv', 'rhythm_7_median', 'rhythm_8_mean', 'rhythm_8_std', 'rhythm_8_maxv', 'rhythm_8_minv', 'rhythm_8_median', 'rhythm_9_mean', 'rhythm_9_std', 'rhythm_9_maxv', 'rhythm_9_minv', 'rhythm_9_median', 'rhythm_10_mean', 'rhythm_10_std', 'rhythm_10_maxv', 'rhythm_10_minv', 'rhythm_10_median', 'rhythm_11_mean', 'rhythm_11_std', 'rhythm_11_maxv', 'rhythm_11_minv', 'rhythm_11_median', 'rhythm_12_mean', 'rhythm_12_std', 'rhythm_12_maxv', 'rhythm_12_minv', 'rhythm_12_median', 'mfcc_0_mean', 'mfcc_0_std', 'mfcc_0_maxv', 'mfcc_0_minv', 'mfcc_0_median', 'mfcc_1_mean', 'mfcc_1_std', 'mfcc_1_maxv', 'mfcc_1_minv', 'mfcc_1_median', 'mfcc_2_mean', 'mfcc_2_std', 'mfcc_2_maxv', 'mfcc_2_minv', 'mfcc_2_median', 'mfcc_3_mean', 'mfcc_3_std', 'mfcc_3_maxv', 'mfcc_3_minv', 'mfcc_3_median', 'mfcc_4_mean', 'mfcc_4_std', 'mfcc_4_maxv', 'mfcc_4_minv', 'mfcc_4_median', 'mfcc_5_mean', 'mfcc_5_std', 'mfcc_5_maxv', 'mfcc_5_minv', 'mfcc_5_median', 'mfcc_6_mean', 'mfcc_6_std', 'mfcc_6_maxv', 'mfcc_6_minv', 'mfcc_6_median', 'mfcc_7_mean', 'mfcc_7_std', 'mfcc_7_maxv', 'mfcc_7_minv', 'mfcc_7_median', 'mfcc_8_mean', 'mfcc_8_std', 'mfcc_8_maxv', 'mfcc_8_minv', 'mfcc_8_median', 'mfcc_9_mean', 'mfcc_9_std', 'mfcc_9_maxv', 'mfcc_9_minv', 'mfcc_9_median', 'mfcc_10_mean', 'mfcc_10_std', 'mfcc_10_maxv', 'mfcc_10_minv', 'mfcc_10_median', 'mfcc_11_mean', 'mfcc_11_std', 'mfcc_11_maxv', 'mfcc_11_minv', 'mfcc_11_median', 'mfcc_12_mean', 'mfcc_12_std', 'mfcc_12_maxv', 'mfcc_12_minv', 'mfcc_12_median', 'poly_0_mean', 'poly_0_std', 'poly_0_maxv', 'poly_0_minv', 'poly_0_median', 'poly_1_mean', 'poly_1_std', 'poly_1_maxv', 'poly_1_minv', 'poly_1_median', 'spectral_centroid_mean', 'spectral_centroid_std', 'spectral_centroid_maxv', 'spectral_centroid_minv', 'spectral_centroid_median', 'spectral_bandwidth_mean', 'spectral_bandwidth_std', 'spectral_bandwidth_maxv', 'spectral_bandwidth_minv', 'spectral_bandwidth_median', 'spectral_contrast_mean', 'spectral_contrast_std', 'spectral_contrast_maxv', 'spectral_contrast_minv', 'spectral_contrast_median', 'spectral_flatness_mean', 'spectral_flatness_std', 'spectral_flatness_maxv', 'spectral_flatness_minv', 'spectral_flatness_median', 'spectral_rolloff_mean', 'spectral_rolloff_std', 'spectral_rolloff_maxv', 'spectral_rolloff_minv', 'spectral_rolloff_median', 'zero_crossings_mean', 'zero_crossings_std', 'zero_crossings_maxv', 'zero_crossings_minv', 'zero_crossings_median', 'RMSE_mean', 'RMSE_std', 'RMSE_maxv', 'RMSE_minv', 'RMSE_median']
STANDARD_SCALER
RFE - 20 features
[('standard_scaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('rfe', RFE(estimator=SVR(C=1.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1,
gamma='scale', kernel='linear', max_iter=-1, shrinking=True,
tol=0.001, verbose=False),
n_features_to_select=20, step=1, verbose=0))]
21
21
transformed training size
[ 1.40339001 -0.46826787 -1.14495583 0.16206821 0.29026766 1.12188432
0.60680057 0.89387526 1.32137089 0.00311893 0.90967555 1.64556559
0.29214439 0.47100284 -0.3969793 0.31780256 -0.54840539 -0.34677929
-0.69461853 -0.60881832]
/Users/jim/Desktop/allie/preprocessing
c_gender_standard_scaler_rfe.pickle
----------------------------------
-%-$-V-|-%-$-V-|-%-$-V-|-%-$-V-|-%-$-
TRANSFORMATION
-%-$-V-|-%-$-V-|-%-$-V-|-%-$-V-|-%-$-
----------------------------------
[7.0, 23.428571428571427, 13.275725891987362, 40.0, 3.0, 29.0, 143.5546875, 0.9958894161283733, 0.548284548384031, 2.9561698164853145, 0.0, 0.9823586435889371, 1.0, 0.0, 1.0, 1.0, 1.0, 0.9563696862586734, 0.004556745090440225, 0.96393999788138, 0.9484953491224131, 0.956450500708107, 0.9159480905407004, 0.0083874210259623, 0.9299425651546245, 0.9015187648369452, 0.9160637212676059, 0.8944452328439548, 0.010436308983995553, 0.9118600181268972, 0.876491746933975, 0.8945884359248597, 0.8770112296385062, 0.012213153676505805, 0.897368483560061, 0.8559758923011408, 0.8771914323071244, 0.8905940594153822, 0.010861457873386392, 0.9086706269489068, 0.871855264076524, 0.8907699627836017, 0.8986618946665619, 0.010095245635220757, 0.9154303336418514, 0.8812084302562171, 0.8988437925782, 0.8940183738781617, 0.01039025341119034, 0.9112831285473728, 0.8760607413483972, 0.8942023162840362, 0.8980899592148434, 0.010095670257151154, 0.9148198685317376, 0.8805923053101389, 0.8982937440953425, 0.888882132228931, 0.01127171631616655, 0.9075145588083179, 0.8692983803153955, 0.8891346978561219, 0.8803505690032647, 0.012156972724147449, 0.9004327206206673, 0.8592151658555779, 0.8806302144175665, 0.8783421648443998, 0.012494808554290946, 0.8989303042965345, 0.8565635990020571, 0.8786581988736766, 0.8633477274070439, 0.013675039594980561, 0.8859283087034104, 0.8395630641775639, 0.8636673866109066, -313.23363896251993, 18.946764320029068, -265.4153881359699, -352.35669009191434, -314.88964335810385, 136.239475379525, 13.46457033057532, 155.28229790095634, 96.67729067600845, 138.31307847807975, -60.109940589659594, 8.90651546650511, -43.00250224341745, -77.22644879310883, -61.59614027580888, 59.959525194997426, 11.49266912690683, 79.92823038661382, 22.593262641790204, 60.14384367187341, -54.39960148805922, 12.978670142489454, -16.69391321594054, -78.18044376664089, -54.04351001558572, 30.023862498118685, 8.714431771268103, 45.984861607171624, -7.969418151448695, 30.779899533210106, -48.79826737490987, 9.404798307829793, -17.32746858770041, -67.85565811008664, -48.67558954047166, 16.438960903373093, 6.676108733267705, 24.75100641115554, -1.8759098025429237, 18.27300445180957, -24.239093865617573, 6.8313516276284245, -9.56759656295116, -40.92277771655667, -24.18878158134608, 3.2516761928923215, 4.2430222382933085, 10.37732827872848, -6.461490621772226, 3.393567465008272, -4.1570109920127685, 5.605424304597271, 5.78957218995748, -18.10767695295411, -3.8190369770110664, -9.46159588572396, 5.81772077466229, 2.7763746636679323, -20.054279810217025, -10.268401482915364, 9.197482271105386, 5.755721680320874, 18.46922506683798, -6.706210697210241, 10.044558505805792, -4.748126927006937e-05, 1.0575334143974938e-05, -2.4722594252240076e-05, -6.952111317908028e-05, -4.773507820227446e-05, 0.40672100602206257, 0.08467855992898438, 0.5757090803234001, 0.22579515457526012, 0.4087367660373401, 2210.951610581014, 212.91019021101542, 2791.2529330926723, 1845.3115106685345, 2223.07457835522, 2063.550185470081, 111.14828141425747, 2287.23164419421, 1816.298268701022, 2073.585928819859, 12.485818860644423, 4.014563343823625, 25.591622605877692, 7.328069768561837, 11.33830589622713, 0.0021384726278483868, 0.004675689153373241, 0.020496303215622902, 0.00027283065719529986, 0.0006564159411936998, 4814.383766867898, 483.0045387722584, 5857.03125, 3682.177734375, 4888.037109375, 0.12172629616477272, 0.0227615872259035, 0.1875, 0.0732421875, 0.1181640625, 0.011859399266541004, 0.0020985447335988283, 0.015743320807814598, 0.006857675965875387, 0.012092416174709797]
-->
[[-1.08260965 -0.98269388 -0.60797492 -0.75483856 -0.81280646 -0.89654763
-0.2878008 -0.57018752 0.31999349 0.91470661 -0.79709927 -0.39215548
-0.52523377 0.54936626 -0.85596512 0.88348636 0.96310551 0.00975297
1.56752723 -0.81022666]]
----------------------------------
gender_ALL_TRANSFORMED.CSV
converting csv...: 100%|█████████████████████| 187/187 [00:00<00:00, 570.70it/s]
transformed_feature_0 ... class_
0 -1.109610 ... 0
1 1.125489 ... 0
2 -1.496244 ... 0
3 -0.971811 ... 0
4 -0.961863 ... 0
.. ... ... ...
182 -1.358248 ... 1
183 -0.376670 ... 1
184 -0.840383 ... 1
185 -0.662551 ... 1
186 -0.580909 ... 1
[187 rows x 21 columns]
writing csv file...
gender_TRAIN_TRANSFORMED.CSV
converting csv...: 100%|█████████████████████| 168/168 [00:00<00:00, 472.53it/s]
transformed_feature_0 ... class_
0 1.378101 ... 0
1 -0.866300 ... 1
2 1.860016 ... 1
3 -0.124242 ... 1
4 1.015606 ... 1
.. ... ... ...
163 1.151959 ... 1
164 -0.157868 ... 1
165 -1.179480 ... 0
166 -0.376670 ... 1
167 -0.580909 ... 1
[168 rows x 21 columns]
writing csv file...
gender_TEST_TRANSFORMED.CSV
converting csv...: 100%|███████████████████████| 19/19 [00:00<00:00, 419.95it/s]
transformed_feature_0 ... class_
0 0.194916 ... 1
1 -0.818428 ... 0
2 -0.089688 ... 1
3 -0.432771 ... 0
4 -0.457341 ... 0
5 -1.054777 ... 1
6 -0.458814 ... 0
7 -1.011486 ... 1
8 1.028686 ... 1
9 0.753718 ... 1
10 -0.959699 ... 0
11 0.515472 ... 1
12 -1.007477 ... 0
13 -0.552799 ... 1
14 -0.642334 ... 1
15 1.778480 ... 1
16 -0.444939 ... 0
17 0.939541 ... 0
18 1.460974 ... 0
[19 rows x 21 columns]
writing csv file...
----------------------------------
___ ______________ _____ _ _____ _ _ _____ ______ ___ _____ ___
| \/ | _ | _ \ ___| | |_ _| \ | | __ \ | _ \/ _ \_ _/ _ \
| . . | | | | | | | |__ | | | | | \| | | \/ | | | / /_\ \| |/ /_\ \
| |\/| | | | | | | | __|| | | | | . ` | | __ | | | | _ || || _ |
| | | \ \_/ / |/ /| |___| |_____| |_| |\ | |_\ \ | |/ /| | | || || | | |
\_| |_/\___/|___/ \____/\_____/\___/\_| \_/\____/ |___/ \_| |_/\_/\_| |_/
----------------------------------
tpot: 0%| | 0/1 [00:00<?, ?it/s]----------------------------------
.... training TPOT
----------------------------------
Requirement already satisfied: tpot==0.11.3 in /usr/local/lib/python3.7/site-packages (0.11.3)
Requirement already satisfied: stopit>=1.1.1 in /usr/local/lib/python3.7/site-packages (from tpot==0.11.3) (1.1.2)
Requirement already satisfied: pandas>=0.24.2 in /usr/local/lib/python3.7/site-packages (from tpot==0.11.3) (0.25.3)
Requirement already satisfied: update-checker>=0.16 in /usr/local/lib/python3.7/site-packages (from tpot==0.11.3) (0.17)
Requirement already satisfied: numpy>=1.16.3 in /usr/local/lib/python3.7/site-packages (from tpot==0.11.3) (1.18.4)
Requirement already satisfied: scipy>=1.3.1 in /usr/local/lib/python3.7/site-packages (from tpot==0.11.3) (1.4.1)
Requirement already satisfied: tqdm>=4.36.1 in /usr/local/lib/python3.7/site-packages (from tpot==0.11.3) (4.43.0)
Requirement already satisfied: deap>=1.2 in /usr/local/lib/python3.7/site-packages (from tpot==0.11.3) (1.3.1)
Requirement already satisfied: scikit-learn>=0.22.0 in /usr/local/lib/python3.7/site-packages (from tpot==0.11.3) (0.22.2.post1)
Requirement already satisfied: joblib>=0.13.2 in /usr/local/lib/python3.7/site-packages (from tpot==0.11.3) (0.15.1)
Requirement already satisfied: python-dateutil>=2.6.1 in /usr/local/lib/python3.7/site-packages (from pandas>=0.24.2->tpot==0.11.3) (2.8.1)
Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/site-packages (from pandas>=0.24.2->tpot==0.11.3) (2020.1)
Requirement already satisfied: requests>=2.3.0 in /usr/local/lib/python3.7/site-packages (from update-checker>=0.16->tpot==0.11.3) (2.24.0)
Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/site-packages (from python-dateutil>=2.6.1->pandas>=0.24.2->tpot==0.11.3) (1.15.0)
Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/site-packages (from requests>=2.3.0->update-checker>=0.16->tpot==0.11.3) (2020.4.5.2)
Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/site-packages (from requests>=2.3.0->update-checker>=0.16->tpot==0.11.3) (3.0.4)
Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/site-packages (from requests>=2.3.0->update-checker>=0.16->tpot==0.11.3) (2.9)
Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/site-packages (from requests>=2.3.0->update-checker>=0.16->tpot==0.11.3) (1.25.9)
Warning: xgboost.XGBClassifier is not available and will not be used by TPOT.
Generation 1 - Current best internal CV score: 0.7976827094474153
Generation 2 - Current best internal CV score: 0.8096256684491978
Generation 3 - Current best internal CV score: 0.8096256684491978
Generation 4 - Current best internal CV score: 0.8096256684491978
Generation 5 - Current best internal CV score: 0.8096256684491978
Generation 6 - Current best internal CV score: 0.8215686274509804
Generation 7 - Current best internal CV score: 0.8215686274509804
Generation 8 - Current best internal CV score: 0.8219251336898395
Generation 9 - Current best internal CV score: 0.8219251336898395
Generation 10 - Current best internal CV score: 0.8276292335115866
tpot: 0%| | 0/1 [03:58<?, ?it/s]
Best pipeline: LinearSVC(Normalizer(input_matrix, norm=max), C=20.0, dual=True, loss=hinge, penalty=l2, tol=0.0001)
/usr/local/lib/python3.7/site-packages/sklearn/svm/_base.py:947: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
"the number of iterations.", ConvergenceWarning)
saving classifier to disk
[1 0 1 0 0 1 0 1 1 0 0 1 1 1 1 1 0 0 0]
Normalized confusion matrix
error making y_probas
error plotting ROC curve
predict_proba only works for or log loss and modified Huber loss.
tpot: 100%|██████████████████████████████████████| 1/1 [04:08<00:00, 248.61s/it]
The result will be a GitHub repo like this, defining the model session and summary. Accuracy metrics will be defined as part of the model training process:
{'accuracy': 0.8947368421052632, 'balanced_accuracy': 0.8944444444444444, 'precision': 0.9, 'recall': 0.9, 'f1_score': 0.9, 'f1_micro': 0.8947368421052632, 'f1_macro': 0.8944444444444444, 'roc_auc': 0.8944444444444444, 'roc_auc_micro': 0.8944444444444444, 'roc_auc_macro': 0.8944444444444444, 'confusion_matrix': [[8, 1], [1, 9]], 'classification_report': ' precision recall f1-score support\n\n males 0.89 0.89 0.89 9\n females 0.90 0.90 0.90 10\n\n accuracy 0.89 19\n macro avg 0.89 0.89 0.89 19\nweighted avg 0.89 0.89 0.89 19\n'}
Click the .GIF below to follow along this example in a video format:
After this, the model will be trained and placed in the models/[sampletype_models] directory. For example, if you trained an audio model with TPOT, the model will be placed in the allie/models/audio_models/ directory.
For automated training, you can alternatively pass through sys.argv[] inputs as follows:
python3 model.py audio 2 c gender males females
Where:
- audio = audio file type
- 2 = 2 classes
- c = classification (r for regression)
- gender = common name of model
- male = first class
- female = second class [via N number of classes]
Goal is to make an output folder like this:
└── gender_tpot_classifier
├── data
│ ├── gender_all.csv
│ ├── gender_all_transformed.csv
│ ├── gender_test.csv
│ ├── gender_test_transformed.csv
│ ├── gender_train.csv
│ └── gender_train_transformed.csv
├── model
│ ├── confusion_matrix.png
│ ├── gender_tpot_classifier.json
│ ├── gender_tpot_classifier.pickle
│ ├── gender_tpot_classifier.py
│ └── gender_tpot_classifier_transform.pickle
├── readme.md
├── requirements.txt
└── settings.json
Now you're ready to go to load these models and make predictions.
Additional instructions can be found here.
Note you can edit the settings.json to change the default featurizer for model training. It is important to train using standard arrays if you plan to put models into production environments, as our database only takes in standard_features for audio files.
Here are the supported file formats for the load directory. Note that if you use alternative file types, the training script may error.
| File type | extension | recommended format |
|---|---|---|
| audio file | .WAV, .MP3 | .WAV |
| text file | .TXT | .TXT |
| image file | .PNG, .JPG | .PNG |
| video file | .MP4 | .MP4 |
| CSV file | .CSV | .CSV |
There are a few other scripts in this folder. The table below describes what each of these scripts does and how to call them.
| Script name | What it does | How to call | Example |
|---|---|---|---|
| delete_features.py | Deletes a specified set of features from a .JSON file (in all folders in train_dir), as specified by the user. | python3 delete_features.py [sampletype] [feature_set] |
python3 delete_features.py audio librosa_features |
| delete_json.py | Deletes all .JSON files from all folders in the train_dir (useful to re-featurize sets of files). | python3 delete_json.py |
python3 delete_json.py |
| rename.py | Renames all the files in a particular directory (both audio files and .JSON files). Note you can manually change this to other file types | python3 rename_files.py [folder] |
python3 rename.py /Users/jim/desktop/allie/train_dir/males |