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Allie

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Allie is a framework for building machine learning models from audio, text, image, video, or .CSV files.

Intended for both beginners and experts, Allie is designed to be easy-to-use for rapid prototyping and easy-to-extend to your desired modeling strategy.

Here are some things that Allie can do:

  • find and download datasets (for quick experiments)
  • annotate, clean, and/or augment audio, text, image, or video datasets (to prepare data for modeling)
  • featurize files using a standard format (via audio, text, image, video, or csv featurizers)
  • transform features (via scikit-learn preprocessing techniques)
  • visualize featurized datasets (via yellowbrick, scikit-learn, and matplotlib libraries)
  • train classification or regression machine learning models (via tpot, autokeras, autopytorch, ludwig, and 15+ other training scripts)
  • make predictions from machine learning models (with all models trained in ./models directory)
  • export data in .CSV file formats (for repeatable machine learning experiments across frameworks)
  • compress machine learning models for deployment (including repositories with readmes)

You can read more about Allie in the wiki documentation.

getting started (Mac or Linux)

First, clone the repository:

git clone [email protected]:jim-schwoebel/allie.git
cd allie 

Set up virtual environment (to ensure consistent operating mode across operating systems).

python3 -m pip install --user virtualenv
python3 -m venv env
source env/bin/activate

Now install required dependencies and perform unit tests to make sure everything works:

python3 setup.py

Note the installation process and unit tests above takes roughly ~10-15 minutes to complete and makes sure that you can featurize, model, and load model files (to make predictions) via your default featurizers and modeling techniques. It may be best to go grab lunch or coffee while waiting. :-)

After everything is done, you can use the Allie CLI by typing in:

python3 allie.py -h

Which should output some ways you can use Allie:

Usage: allie.py [options]

Options:
  -h, --help            show this help message and exit
  --c=command, --command=command
                        the target command (annotate API = 'annotate',
                        augmentation API = 'augment',  cleaning API = 'clean',
                        datasets API = 'data',  features API = 'features',
                        model prediction API = 'predict',  preprocessing API =
                        'transform',  model training API = 'train',  testing
                        API = 'test',  visualize API = 'visualize',
                        list/change default settings = 'settings')
  --p=problemtype, --problemtype=problemtype
                        specify the problem type ('c' = classification or 'r'
                        = regression)
  --s=sampletype, --sampletype=sampletype
                        specify the type files that you'd like to operate on
                        (e.g. 'audio', 'text', 'image', 'video', 'csv')
  --n=common_name, --name=common_name
                        specify the common name for the model (e.g. 'gender'
                        for a male/female problem)
  --i=class_, --class=class_
                        specify the class that you wish to annotate (e.g.
                        'male')
  --d=dir, --dir=dir    an array of the target directory (or directories) that
                        contains sample files for the annotation API,
                        prediction API, features API, augmentation API,
                        cleaning API, and preprocessing API (e.g.
                        '/Users/jim/desktop/allie/train_dir/teens/')

For more information on how to use the Allie CLI, check out the Allie CLI tutorial or any of the links below:

Quick examples

The below examples assume the default settings.json configuration in Allie. Results may vary if you change this file.

Collecting data

To illustrate a quick example, we can pull some sample audio data from this GitHub repository, separating males (x50) from females (x50).

This seed_test.py script creates two datasets in the train_dir folder, one full of audio files of males and the other full of audio files of females. This data will be used for the rest of the demo sections listed here.

cd /Users/jim/desktop/allie
cd datasets
python3 seed_test.py
---------------
Cloning into 'sample_voice_data'...
remote: Enumerating objects: 119, done.
remote: Counting objects: 100% (119/119), done.
remote: Compressing objects: 100% (115/115), done.
remote: Total 119 (delta 5), reused 108 (delta 2), pack-reused 0
Receiving objects: 100% (119/119), 18.83 MiB | 7.43 MiB/s, done.
Resolving deltas: 100% (5/5), done.

You can easily test if the files are in there with:

cd ..
cd train_dir
ls

Which should output:

[email protected] train_dir % ls
README.md		delete_json.py		females
delete_features.py	featurize_csv.py	males

Click the .GIF below to follow along this example in a video format:

Annotating data

You can simply annotate by typing this into the terminal:

cd /Users/jim/desktop/allie
cd annotation
python3 annotate.py -d /Users/jim/desktop/allie/train_dir/males/ -s audio -c male -p classification

What results is annotated folders in .JSON format following the standard dictionary.

{"sampletype": "audio", "transcripts": {"audio": {}, "text": {}, "image": {}, "video": {}, "csv": {}}, "features": {"audio": {}, "text": {}, "image": {}, "video": {}, "csv": {}}, "models": {"audio": {}, "text": {}, "image": {}, "video": {}, "csv": {}}, "labels": [{"male": {"value": 1.0, "datetime": "2020-08-03 14:06:53.101763", "filetype": "audio", "file": "1.wav", "problemtype": "classification", "annotate_dir": "/Users/jim/desktop/allie/train_dir/males"}}], "errors": [], "settings": {"version": "1.0.0", "augment_data": false, "balance_data": true, "clean_data": false, "create_csv": true, "default_audio_augmenters": ["augment_tsaug"], "default_audio_cleaners": ["clean_mono16hz"], "default_audio_features": ["librosa_features"], "default_audio_transcriber": ["deepspeech_dict"], "default_csv_augmenters": ["augment_ctgan_regression"], "default_csv_cleaners": ["clean_csv"], "default_csv_features": ["csv_features"], "default_csv_transcriber": ["raw text"], "default_dimensionality_reducer": ["pca"], "default_feature_selector": ["rfe"], "default_image_augmenters": ["augment_imaug"], "default_image_cleaners": ["clean_greyscale"], "default_image_features": ["image_features"], "default_image_transcriber": ["tesseract"], "default_outlier_detector": ["isolationforest"], "default_scaler": ["standard_scaler"], "default_text_augmenters": ["augment_textacy"], "default_text_cleaners": ["remove_duplicates"], "default_text_features": ["nltk_features"], "default_text_transcriber": ["raw text"], "default_training_script": ["tpot"], "default_video_augmenters": ["augment_vidaug"], "default_video_cleaners": ["remove_duplicates"], "default_video_features": ["video_features"], "default_video_transcriber": ["tesseract (averaged over frames)"], "dimension_number": 2, "feature_number": 20, "model_compress": false, "reduce_dimensions": false, "remove_outliers": true, "scale_features": true, "select_features": true, "test_size": 0.1, "transcribe_audio": true, "transcribe_csv": true, "transcribe_image": true, "transcribe_text": true, "transcribe_video": true, "visualize_data": false, "transcribe_videos": true}}

After you annotate, you can create a nicely formatted .CSV for machine learning:

cd /Users/jim/desktop/allie
cd annotation
python3 create_csv.py -d /Users/jim/desktop/allie/train_dir/males/ -s audio -c male -p classification

Click the .GIF below to follow along this example in a video format:

Augmenting data

To augment folders of files, type this into the terminal:

cd /Users/jim/desktop/allie
cd augmentation/audio_augmentation
python3 augment.py /Users/jim/desktop/allie/train_dir/males/
python3 augment.py /Users/jim/desktop/allie/train_dir/females/

You should now have 2x the data in each folder. Here is a sample audio file and augmented audio file (in females) folder, for reference:

Click the .GIF below to follow along this example in a video format:

Cleaning data

To clean folders of files, type this into the terminal:

cd /Users/jim/desktop/allie
cd cleaning/audio_cleaning
python3 clean.py /Users/jim/desktop/allie/train_dir/males/
python3 clean.py /Users/jim/desktop/allie/train_dir/females/

Click the .GIF below to follow along this example in a video format:

Featurizing data

To featurize folders of files, type this into the terminal:

cd /Users/jim/desktop/allie
cd features/audio_features
python3 featurize.py /Users/jim/desktop/allie/train_dir/males/

What results are featurized audio files in .JSON format, following the standard dictionary. In this case, librosa_features ahve been added to the audio features dictionary element:

{"sampletype": "audio", "transcripts": {"audio": {"deepspeech_dict": "no time to fear with which segnatura"}, "text": {}, "image": {}, "video": {}, "csv": {}}, "features": {"audio": {"librosa_features": {"features": [26.0, 91.6923076923077, 51.970234995079515, 168.0, 3.0, 91.5, 151.99908088235293, 1.4653875809266168, 1.034836765896441, 6.041317273120036, 0.0, 1.1416813305224416, 1.0, 0.0, 1.0, 1.0, 1.0, 0.8034238194742891, 0.011214064753885858, 0.825403850923042, 0.7916342653642267, 0.799591131419195, 0.6655924418356907, 0.011330648308197012, 0.6913809809074571, 0.6541308272200052, 0.661418338298795, 0.6733986003222069, 0.015596609024721262, 0.7120299672460608, 0.6587366189344313, 0.6663189274679151, 0.6465513378063461, 0.025089929067247278, 0.7051369537517457, 0.6222968652297333, 0.6355192254778563, 0.6149336085508313, 0.018343891143310215, 0.6627315206635452, 0.5977577122027029, 0.607582407218563, 0.647661411671485, 0.008459482998415297, 0.6713073144386539, 0.6386408867240191, 0.6452831890619676, 0.699964101062811, 0.007615472147513634, 0.7189362743395604, 0.6881770842137894, 0.6979190256767938, 0.7291497355453966, 0.00940911939072975, 0.7401396411729065, 0.7017699200214637, 0.7331228695983505, 0.7531264693954148, 0.01329521814226255, 0.763276239791762, 0.7116522984632485, 0.7590587373746718, 0.7350729652806612, 0.03153112064046178, 0.7660970571880834, 0.657206917912684, 0.7470611927003584, 0.6322639720282205, 0.017938497171685913, 0.6615311610839943, 0.5941319788201291, 0.6343007353689181, 0.5778206400146562, 0.017037307606750474, 0.6125509247436416, 0.5514687732413751, 0.5754841853855134, -201.3533386630615, 47.15330192057996, -102.18535523700777, -502.49179220012513, -201.31156601864114, 125.25603463854729, 18.082299070656465, 162.0310670688952, 63.261254376704564, 127.81548416353635, -71.79877076064429, 25.856723357072436, 0.00849312238186889, -135.65333506936406, -70.68911674995219, 36.022947214229916, 17.88923654191366, 88.64728797287137, -4.5486056006807685, 32.97698049436967, -43.81875187482729, 14.559312843201848, -11.695474808994188, -92.5127540007665, -43.3715458030925, -2.8686813657134924, 15.41813093478462, 45.52354221029802, -46.72785054356835, -3.558833340504383, -27.922593394058616, 10.927878425731766, -1.4375539667895811, -55.265896216864945, -26.81749586891489, -5.505759781242237, 11.987368006311469, 29.15642000598597, -32.415122640721314, -5.511091433999935, -15.823860504423996, 9.451418989514549, 5.241598458345977, -37.9588985258688, -15.755382749864523, -0.4895271220805938, 9.193396110591832, 21.927443663882503, -17.64191390126784, -1.8424862936634168, -9.022651931057775, 9.128291169735268, 18.549517520727974, -30.5660411806481, -8.34285803735538, -9.278437145276117, 8.646155204335475, 14.76706895840456, -27.949477962308606, -9.825154645141577, -0.5851781562774249, 7.5172603841211965, 15.958013214521532, -19.188487080387258, -1.4245433010743693, -0.0002136159172391847, 0.0001244699595478107, -4.3705042965701695e-06, -0.0006147477962819087, -0.00018150223332853152, 1.798108486545567, 1.0501312811041865, 5.152526911465508, 0.0364132947716482, 1.5099976502325472, 1974.162593266179, 480.2110921639575, 3767.7561717054723, 1207.1282130698876, 1914.9902608895832, 1597.477406458397, 300.1835303257206, 2611.5607852021894, 1096.7664088833956, 1524.2274402971789, 19.140024880982267, 5.679892757483778, 63.887253040125415, 7.787955276296776, 18.524605940498716, 0.00018343908595852554, 0.0003219500940758735, 0.003092152765020728, 9.228861017618328e-06, 7.437301246682182e-05, 3716.331787109375, 970.2260872230195, 6556.8603515625, 1894.921875, 3692.9443359375, 0.11654459635416667, 0.0477755604304587, 0.28662109375, 0.04052734375, 0.1044921875, 0.07000089436769485, 0.042519740760326385, 0.18653172254562378, 0.010408219881355762, 0.06168703734874725], "labels": ["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"]}}, "text": {}, "image": {}, "video": {}, "csv": {}}, "models": {"audio": {}, "text": {}, "image": {}, "video": {}, "csv": {}}, "labels": ["females"], "errors": [], "settings": {"version": "1.0.0", "augment_data": false, "balance_data": true, "clean_data": false, "create_csv": true, "default_audio_augmenters": ["augment_tsaug"], "default_audio_cleaners": ["clean_mono16hz"], "default_audio_features": ["librosa_features"], "default_audio_transcriber": ["deepspeech_dict"], "default_csv_augmenters": ["augment_ctgan_regression"], "default_csv_cleaners": ["clean_csv"], "default_csv_features": ["csv_features"], "default_csv_transcriber": ["raw text"], "default_dimensionality_reducer": ["pca"], "default_feature_selector": ["rfe"], "default_image_augmenters": ["augment_imaug"], "default_image_cleaners": ["clean_greyscale"], "default_image_features": ["image_features"], "default_image_transcriber": ["tesseract"], "default_outlier_detector": ["isolationforest"], "default_scaler": ["standard_scaler"], "default_text_augmenters": ["augment_textacy"], "default_text_cleaners": ["remove_duplicates"], "default_text_features": ["nltk_features"], "default_text_transcriber": ["raw text"], "default_training_script": ["tpot"], "default_video_augmenters": ["augment_vidaug"], "default_video_cleaners": ["remove_duplicates"], "default_video_features": ["video_features"], "default_video_transcriber": ["tesseract (averaged over frames)"], "dimension_number": 2, "feature_number": 20, "model_compress": false, "reduce_dimensions": true, "remove_outliers": true, "scale_features": true, "select_features": true, "test_size": 0.1, "transcribe_audio": true, "transcribe_csv": true, "transcribe_image": true, "transcribe_text": true, "transcribe_video": true, "visualize_data": false, "transcribe_videos": true}}

Click the .GIF below to follow along this example in a video format:

Transforming data

To transform two folders of files with a classification modeling goal, type this into the terminal:

cd /Users/jim/desktop/allie
cd preprocessing
python3 transform.py audio c gender males females

What results are a few files, with a summary .JSON file (c_gender_standard_scaler_rfe.json) that elaborates upon the transformer in the ./preprocessing/audio_transformer directory:

{"estimators": "[('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,\n                  gamma='scale', kernel='linear', max_iter=-1, shrinking=True,\n                  tol=0.001, verbose=False),\n    n_features_to_select=20, step=1, verbose=0))]", "settings": {"version": "1.0.0", "augment_data": false, "balance_data": true, "clean_data": false, "create_csv": true, "default_audio_augmenters": ["augment_tsaug"], "default_audio_cleaners": ["clean_mono16hz"], "default_audio_features": ["librosa_features"], "default_audio_transcriber": ["deepspeech_dict"], "default_csv_augmenters": ["augment_ctgan_regression"], "default_csv_cleaners": ["clean_csv"], "default_csv_features": ["csv_features"], "default_csv_transcriber": ["raw text"], "default_dimensionality_reducer": ["pca"], "default_feature_selector": ["rfe"], "default_image_augmenters": ["augment_imaug"], "default_image_cleaners": ["clean_greyscale"], "default_image_features": ["image_features"], "default_image_transcriber": ["tesseract"], "default_outlier_detector": ["isolationforest"], "default_scaler": ["standard_scaler"], "default_text_augmenters": ["augment_textacy"], "default_text_cleaners": ["remove_duplicates"], "default_text_features": ["nltk_features"], "default_text_transcriber": ["raw text"], "default_training_script": ["tpot"], "default_video_augmenters": ["augment_vidaug"], "default_video_cleaners": ["remove_duplicates"], "default_video_features": ["video_features"], "default_video_transcriber": ["tesseract (averaged over frames)"], "dimension_number": 2, "feature_number": 20, "model_compress": false, "reduce_dimensions": false, "remove_outliers": true, "scale_features": true, "select_features": true, "test_size": 0.1, "transcribe_audio": true, "transcribe_csv": true, "transcribe_image": true, "transcribe_text": true, "transcribe_video": true, "visualize_data": false, "transcribe_videos": true}, "classes": [0, 1], "sample input X": [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], "sample input Y": 1, "sample transformed X": [1.403390013815334, -0.46826787078753823, -1.1449558304944494, 0.16206820805906216, 0.29026766476533494, 1.121884315543298, 0.6068005729105246, 0.8938752562067679, 1.321370886418425, 0.003118933840524784, 0.9096755549417692, 1.6455655890558802, 0.29214439046692964, 0.47100284419336996, -0.3969793022778641, 0.3178025570312262, -0.5484053895612412, -0.34677929105059246, -0.6946185268998197, -0.6088183224560646], "sample transformed y": 1}

Click the .GIF below to follow along this example in a video format:

Modeling data

classification problem

To now model both males and females as a binary gender classification problem, type this into the terminal:

cd /Users/jim/desktop/allie
cd 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:

regression problem

To model a regression problem, you need a .CSV file with annotations and files. It is best to produce these files with the annotate.py script, but you can also use datasets created within Allie for regression modeling.

All you need to do is follow the similar steps for modeling, specifying a regression target and the .CSV file of interest:

python3 model.py
                                                                             
                                                                             
               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? 
r
what is the name of the spreadsheet (in ./train_dir) used for prediction? 

 available: ['gender_all.csv']

gender_all.csv
how many classes would you like to model? (188 available) 
1
these are the available classes: ['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', 'class_']
what is class #1 
class_
what is the 1-word common name for the problem you are working on? (e.g. gender for male/female classification) 
gender

It will then output the regression model in the proper folder (like this), using TPOT as a model trainer. You will also get some awesome stats on the regression modeling sesssion, like in the .JSON file below:

{"sample type": "csv", "created date": "2020-08-03 15:29:43.786976", "device info": {"time": "2020-08-03 15:29", "timezone": ["EST", "EDT"], "operating system": "Darwin", "os release": "19.5.0", "os version": "Darwin Kernel Version 19.5.0: Tue May 26 20:41:44 PDT 2020; root:xnu-6153.121.2~2/RELEASE_X86_64", "cpu data": {"memory": [8589934592, 2577022976, 70.0, 4525428736, 107941888, 2460807168, 2122092544, 2064621568], "cpu percent": 59.1, "cpu times": [22612.18, 0.0, 12992.38, 102624.04], "cpu count": 4, "cpu stats": [110955, 504058, 130337047, 518089], "cpu swap": [2147483648, 1096548352, 1050935296, 51.1, 44743286784, 329093120], "partitions": [["/dev/disk1s6", "/", "apfs", "ro,local,rootfs,dovolfs,journaled,multilabel"], ["/dev/disk1s5", "/System/Volumes/Data", "apfs", "rw,local,dovolfs,dontbrowse,journaled,multilabel"], ["/dev/disk1s4", "/private/var/vm", "apfs", "rw,local,dovolfs,dontbrowse,journaled,multilabel"], ["/dev/disk1s1", "/Volumes/Macintosh HD - Data", "apfs", "rw,local,dovolfs,journaled,multilabel"]], "disk usage": [499963174912, 10985529344, 317145075712, 3.3], "disk io counters": [1689675, 1773144, 52597518336, 34808844288, 1180797, 1136731], "battery": [100, -2, true], "boot time": 1596411904.0}, "space left": 317.145075712}, "session id": "fc54dd66-d5bc-11ea-9c75-acde48001122", "classes": ["class_"], "problem type": "regression", "model name": "gender_tpot_regression.pickle", "model type": "tpot", "metrics": {"mean_absolute_error": 0.37026379788606023, "mean_squared_error": 0.16954440031335424, "median_absolute_error": 0.410668441980656, "r2_score": 0.3199385720764347}, "settings": {"version": "1.0.0", "augment_data": false, "balance_data": true, "clean_data": false, "create_csv": true, "default_audio_augmenters": ["augment_tsaug"], "default_audio_cleaners": ["clean_mono16hz"], "default_audio_features": ["librosa_features"], "default_audio_transcriber": ["deepspeech_dict"], "default_csv_augmenters": ["augment_ctgan_regression"], "default_csv_cleaners": ["clean_csv"], "default_csv_features": ["csv_features"], "default_csv_transcriber": ["raw text"], "default_dimensionality_reducer": ["pca"], "default_feature_selector": ["rfe"], "default_image_augmenters": ["augment_imaug"], "default_image_cleaners": ["clean_greyscale"], "default_image_features": ["image_features"], "default_image_transcriber": ["tesseract"], "default_outlier_detector": ["isolationforest"], "default_scaler": ["standard_scaler"], "default_text_augmenters": ["augment_textacy"], "default_text_cleaners": ["remove_duplicates"], "default_text_features": ["nltk_features"], "default_text_transcriber": ["raw text"], "default_training_script": ["tpot"], "default_video_augmenters": ["augment_vidaug"], "default_video_cleaners": ["remove_duplicates"], "default_video_features": ["video_features"], "default_video_transcriber": ["tesseract (averaged over frames)"], "dimension_number": 2, "feature_number": 20, "model_compress": false, "reduce_dimensions": false, "remove_outliers": true, "scale_features": false, "select_features": false, "test_size": 0.1, "transcribe_audio": false, "transcribe_csv": true, "transcribe_image": true, "transcribe_text": true, "transcribe_video": true, "transcribe_videos": true, "visualize_data": false, "default_dimensionionality_reducer": ["pca"]}, "transformer name": "", "training data": [], "sample X_test": [30.0, 116.1, 68.390715744171, 224.0, 3.0, 115.5, 129.19921875, 1.579895074162117, 1.4053805862299766, 6.915237601339313, 0.0, 1.1654598038099069, 0.0, 0.0, 0.0, 0.0, 0.0, 0.8033179369485901, 0.00438967342343324, 0.8140795129312649, 0.7979309783326958, 0.80255447893579, 0.5772101965904585, 0.025367026843705915, 0.6147904436358145, 0.5452462503889344, 0.5720709525572024, 0.5251607032640779, 0.031273364291655614, 0.5651684602891733, 0.4833782607526296, 0.522481114581999, 0.53067387207457, 0.01636309315550051, 0.5760527497162795, 0.5083941678429416, 0.5308772078223155, 0.5383483269837346, 0.02398538849569036, 0.6138641187358237, 0.5148823529890311, 0.5317355191905834, 0.5590921868458475, 0.018941050706796927, 0.6185565218733067, 0.5391848127954322, 0.5515129204797803, 0.5653692033981255, 0.022886171192539908, 0.6170498591126126, 0.5187020777516459, 0.5693268285980656, 0.5428369240411614, 0.011543007163874491, 0.5837123204211986, 0.5208221399174541, 0.5415414663324902, 0.4946660644711973, 0.021472694373470352, 0.5215764169994959, 0.4640787039752625, 0.4952267598817138, 0.4798469011394895, 0.02593484469896265, 0.5172960598832023, 0.4449712627305569, 0.4777149108114186, 0.4993938744598669, 0.01849048457494309, 0.5651910299787914, 0.4822436630327371, 0.4950261489562563, 0.5363930497563161, 0.0376443504751349, 0.6330907702118795, 0.4816294954352716, 0.5249507027509328, -235.4678661326307, 61.51638081120653, -119.29458629496251, -362.1632462796749, -227.60500825042942, 163.92070611988834, 47.05955903012367, 237.9764586528294, 41.986380826321785, 172.71493170004138, 9.237411399943188, 25.868443694231683, 61.477039729510096, -75.39528620218707, 9.629797757209056, 38.85787728431835, 25.651975918739637, 120.33667371104372, -9.003575689525233, 36.13886469019118, -3.813926397129359, 18.466559976322753, 45.395818864794386, -54.58126572108478, -3.563646356257889, 28.49882430361086, 15.286105184256387, 72.2886732962803, 0.03239718043784112, 26.491533722920998, -19.866746887564343, 16.46528562102129, 9.928420130258688, -61.42422346209003, -17.134010559191154, 4.917765483447672, 13.106589177321654, 36.30054941946764, -28.88492762419697, 4.470641784765922, -7.5214435695300805, 11.456845078656613, 24.68530842159717, -33.23468909518539, -7.800944005694487, 1.7653313822916499, 10.137823325108423, 26.38688279047729, -22.507646864346647, 2.1230603462314384, 2.9722994596741263, 9.920580299259306, 29.09083383516883, -28.462312178142557, 3.1356694281534625, -8.31659816437322, 9.321735116288234, 14.977416272339756, -29.19924207526083, -7.200232618719922, 10.020856138237773, 9.605360863583002, 33.70453001221575, -10.34310153320585, 8.538943192527702, -0.0003117740953455404, 0.0002093530273784296, -3.649852038234921e-05, -0.0008609846033373115, -0.00024944132582088046, 2.427670449088513, 1.573081810523066, 6.574603060966783, 0.2961628052414745, 1.8991203106986, 1122.5579040699354, 895.7957759390358, 4590.354474064802, 349.53842801686966, 800.0437543350607, 1384.7323846043691, 519.4846094956321, 2642.151716668925, 703.4646482237979, 1229.7584170111122, 22.097758701059746, 6.005214057147793, 54.922406822231686, 8.895233246285754, 22.047151155860252, 6.146541272755712e-05, 0.00013457647582981735, 0.0006881643203087151, 5.692067475138174e-07, 8.736528798181098e-06, 2087.3572470319323, 1731.5818839146564, 6535.3271484375, 409.130859375, 1421.19140625, 0.05515445892467249, 0.07680443213522453, 0.46142578125, 0.0078125, 0.0302734375, 0.12412750720977785, 0.07253565639257431, 0.29952874779701233, 0.010528072714805605, 0.10663044452667236], "sample y_test": 0}

Click the .GIF below to follow along this example in a video format:

Making predictions

To make predictions using the machine learning model that you just trained, type this into the terminal:

cd /Users/jim/desktop/allie
cd models
python3 load.py

What results is featurized folders with model predictions in .JSON format following the standard dictionary, as shown below:

{"sampletype": "audio", "transcripts": {"audio": {"deepspeech_dict": "this is some testator testing once you three i am testing for ale while machinery framework to see his accretion the new sample test"}, "text": {}, "image": {}, "video": {}, "csv": {}}, "features": {"audio": {"librosa_features": {"features": [48.0, 204.70833333333334, 114.93240011947118, 396.0, 14.0, 216.5, 103.359375, 1.8399430940147428, 1.9668646890049444, 11.385597904924593, 0.0, 1.0559244294941723, 1.0, 0.0, 1.0, 1.0, 1.0, 0.7548547717034835, 0.010364651324331484, 0.7781876333922462, 0.7413898270731765, 0.7531146388573189, 0.5329470301378001, 0.017993121900389288, 0.5625446261639591, 0.49812177879164954, 0.5362475050227643, 0.5019325152826378, 0.014070606086479512, 0.523632811868841, 0.46894673564752315, 0.5016733890463346, 0.47905132092405744, 0.02472744944944913, 0.5170032241543769, 0.408032583763636, 0.481510183943566, 0.47211244429901056, 0.018043067999864118, 0.4986083755424441, 0.4084943475419884, 0.47487594610615297, 0.4698145764425497, 0.02481760404009747, 0.5249353704873062, 0.4293713399428569, 0.4722612320098678, 0.45261508487773017, 0.026497663310172545, 0.49848564789957234, 0.40880741566998524, 0.4507269108715201, 0.4486803104555413, 0.058559460166888094, 0.5292193402660791, 0.3401144705267203, 0.44999945618536435, 0.47497774707770735, 0.06545659069313127, 0.5736851049778624, 0.37925421500129, 0.4734915617563768, 0.4650337731799947, 0.06320658864729298, 0.5675856011170606, 0.3828128296325481, 0.45491284769941215, 0.4336677569640048, 0.06580364398487831, 0.5229786825561087, 0.3254973876934075, 0.4435446804048719, 0.4510229261935718, 0.0716424867984984, 0.5607997826027251, 0.3319068941555564, 0.45336899240905365, -378.4693712461592, 123.45005738361948, -131.02074973363048, -645.6119532302674, -365.0407612849682, 108.01722016743142, 78.5850621057939, 244.19279156346005, -109.89544987268641, 113.87757464191944, -18.990339871317058, 38.97227759803155, 80.46313291288668, -113.14922433281748, -19.5478460234633, 25.85348830525823, 36.66801973350443, 140.72102808980202, -59.74682246793187, 18.3196627309548, 25.890819294565695, 28.110070916600474, 109.71209190044716, -32.50655086525428, 24.126562365562382, -12.77779324195114, 25.980150189124338, 37.34024720564918, -89.18596268298815, -14.092855104596493, -14.213402047550273, 17.851386217883952, 24.416921204215857, -53.80916251929509, -15.616460366626296, -11.056262156053059, 18.131479541957944, 25.019042211813467, -65.95011982036516, -10.115261093647717, -2.111560667454096, 11.800353875032327, 33.815281150727785, -35.047615612670526, -2.4632489045982657, -12.855548041442455, 13.841955462451525, 26.49950045235625, -54.65905146286438, -12.258563565004795, -5.991988010947961, 11.560727147262314, 26.699383611419385, -46.86210002294128, -5.08389478450145, -11.905883972886778, 13.110884275285521, 18.96898208296976, -55.222181120197234, -8.889351847151506, -13.282554457300717, 9.363802595261776, 13.125079504552438, -42.40351688080857, -12.904730673116855, -6.647081175227956e-05, 6.790962221819154e-05, 3.898538767970233e-05, -0.0003530532719088282, -5.176161063821292e-05, 0.5775604310470552, 0.5363262958114443, 3.0171051694951547, 0.005876029108677461, 0.447613631105005, 2196.6402149427804, 1460.1082170800585, 6848.122696727527, 474.45532202867423, 1779.7575344580457, 1879.6573011499802, 758.0548156953982, 3968.436183431614, 710.7057371268927, 1783.9133839417857, 25.057721821734972, 7.417488037600184, 48.54069273302066, 7.980294433517432, 26.382808285840404, 0.02705797180533409, 0.049401603639125824, 0.22989588975906372, 2.3204531316878274e-05, 0.0016842428594827652, 3896.30511090472, 2618.9936438064337, 9829.9072265625, 484.4970703125, 2993.115234375, 0.13837594696969696, 0.11062751539644003, 0.62060546875, 0.01220703125, 0.10009765625, 0.025540588423609734, 0.02010413259267807, 0.09340725094079971, 0.00015651443391107023, 0.02306547947227955], "labels": ["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"]}}, "text": {}, "image": {}, "video": {}, "csv": {}}, "models": {"audio": {"males": [{"sample type": "audio", "created date": "2020-08-03 12:55:08.238841", "device info": {"time": "2020-08-03 12:55", "timezone": ["EST", "EDT"], "operating system": "Darwin", "os release": "19.5.0", "os version": "Darwin Kernel Version 19.5.0: Tue May 26 20:41:44 PDT 2020; root:xnu-6153.121.2~2/RELEASE_X86_64", "cpu data": {"memory": [8589934592, 3035197440, 64.7, 4487892992, 379949056, 2523181056, 2408304640, 1964711936], "cpu percent": 66.0, "cpu times": [14797.03, 0.0, 9385.82, 76944.46], "cpu count": 4, "cpu stats": [153065, 479666, 89106680, 587965], "cpu swap": [2147483648, 1174405120, 973078528, 54.7, 30354079744, 203853824], "partitions": [["/dev/disk1s6", "/", "apfs", "ro,local,rootfs,dovolfs,journaled,multilabel"], ["/dev/disk1s5", "/System/Volumes/Data", "apfs", "rw,local,dovolfs,dontbrowse,journaled,multilabel"], ["/dev/disk1s4", "/private/var/vm", "apfs", "rw,local,dovolfs,dontbrowse,journaled,multilabel"], ["/dev/disk1s1", "/Volumes/Macintosh HD - Data", "apfs", "rw,local,dovolfs,journaled,multilabel"]], "disk usage": [499963174912, 10985529344, 320581328896, 3.3], "disk io counters": [1283981, 844586, 35781873664, 17365774336, 850754, 779944], "battery": [100, -2, true], "boot time": 1596411904.0}, "space left": 320.581328896}, "session id": "867c4358-d5a9-11ea-8720-acde48001122", "classes": ["males", "females"], "problem type": "classification", "model name": "gender_tpot_classifier.pickle", "model type": "tpot", "metrics": {"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"}, "settings": {"version": "1.0.0", "augment_data": false, "balance_data": true, "clean_data": false, "create_csv": true, "default_audio_augmenters": ["augment_tsaug"], "default_audio_cleaners": ["clean_mono16hz"], "default_audio_features": ["librosa_features"], "default_audio_transcriber": ["deepspeech_dict"], "default_csv_augmenters": ["augment_ctgan_regression"], "default_csv_cleaners": ["clean_csv"], "default_csv_features": ["csv_features"], "default_csv_transcriber": ["raw text"], "default_dimensionality_reducer": ["pca"], "default_feature_selector": ["rfe"], "default_image_augmenters": ["augment_imaug"], "default_image_cleaners": ["clean_greyscale"], "default_image_features": ["image_features"], "default_image_transcriber": ["tesseract"], "default_outlier_detector": ["isolationforest"], "default_scaler": ["standard_scaler"], "default_text_augmenters": ["augment_textacy"], "default_text_cleaners": ["remove_duplicates"], "default_text_features": ["nltk_features"], "default_text_transcriber": ["raw text"], "default_training_script": ["tpot"], "default_video_augmenters": ["augment_vidaug"], "default_video_cleaners": ["remove_duplicates"], "default_video_features": ["video_features"], "default_video_transcriber": ["tesseract (averaged over frames)"], "dimension_number": 2, "feature_number": 20, "model_compress": false, "reduce_dimensions": false, "remove_outliers": true, "scale_features": true, "select_features": true, "test_size": 0.1, "transcribe_audio": true, "transcribe_csv": true, "transcribe_image": true, "transcribe_text": true, "transcribe_video": true, "visualize_data": false, "transcribe_videos": true}, "transformer name": "gender_tpot_classifier_transform.pickle", "training data": ["gender_all.csv", "gender_train.csv", "gender_test.csv", "gender_all_transformed.csv", "gender_train_transformed.csv", "gender_test_transformed.csv"], "sample X_test": [0.19491584410160165, 2.278239927977625, 1.9809968520802117, 0.01621731265879942, 0.15713016963065518, 0.6373734371406007, 0.5565326177000756, 0.21607641781209055, 1.5729652666810199, 0.4175324163804035, 0.25821087005791604, 1.688251084321436, 0.641181793964938, 0.8245062752279405, 3.328186152340374, -3.566702513086108, -0.7896923143197454, -0.33315803775179953, -0.9991381480355723, 3.3414140426072754], "sample y_test": 1}]}, "text": {}, "image": {}, "video": {}, "csv": {}}, "labels": ["load_dir"], "errors": [], "settings": {"version": "1.0.0", "augment_data": false, "balance_data": true, "clean_data": false, "create_csv": true, "default_audio_augmenters": ["augment_tsaug"], "default_audio_cleaners": ["clean_mono16hz"], "default_audio_features": ["librosa_features"], "default_audio_transcriber": ["deepspeech_dict"], "default_csv_augmenters": ["augment_ctgan_regression"], "default_csv_cleaners": ["clean_csv"], "default_csv_features": ["csv_features"], "default_csv_transcriber": ["raw text"], "default_dimensionality_reducer": ["pca"], "default_feature_selector": ["rfe"], "default_image_augmenters": ["augment_imaug"], "default_image_cleaners": ["clean_greyscale"], "default_image_features": ["image_features"], "default_image_transcriber": ["tesseract"], "default_outlier_detector": ["isolationforest"], "default_scaler": ["standard_scaler"], "default_text_augmenters": ["augment_textacy"], "default_text_cleaners": ["remove_duplicates"], "default_text_features": ["nltk_features"], "default_text_transcriber": ["raw text"], "default_training_script": ["tpot"], "default_video_augmenters": ["augment_vidaug"], "default_video_cleaners": ["remove_duplicates"], "default_video_features": ["video_features"], "default_video_transcriber": ["tesseract (averaged over frames)"], "dimension_number": 2, "feature_number": 20, "model_compress": false, "reduce_dimensions": false, "remove_outliers": true, "scale_features": true, "select_features": true, "test_size": 0.1, "transcribe_audio": true, "transcribe_csv": true, "transcribe_image": true, "transcribe_text": true, "transcribe_video": true, "visualize_data": false, "transcribe_videos": true}}

Click the .GIF below to follow along this example in a video format:

Visualizing data

To visualize multiple folders of featurized files (in this case males and females folders of audio files in the ./train_dir), type this into the terminal:

cd /Users/jim/desktop/allie
cd visualize
python3 visualize.py audio males females

Note that visualization capabilities are restricted to classification problems for Allie version 1.0.

Click the .GIF below to follow along this example in a video format:

Settings

Allie has multiple default settings for model training to help you start out with the framework. Allie has been built so that the settings you specify in lists are done serially, which can be useful to construct machine learning models from multiple back-end model trainers in a single session. Settings can be modified in the settings.json file.

Here are some settings that you can modify in this settings.json file and the various options for these settings:

setting description default setting all options
version version of Allie release 1.0 1.0
augment_data whether or not to implement data augmentation policies during the model training process via default augmentation scripts. True True, False
balance_data whether or not to balance datasets during the model training process. True True, False
clean_data whether or not to clean datasets during the model training process via default cleaning scripts. False True, False
create_csv whether or not to output datasets in a nicely formatted .CSV as part of the model training process (outputs to ./data folder in model repositories) True True, False
default_audio_augmenters the default augmentation strategies used during audio modeling if augment_data == True ["augment_tsaug"] ["augment_tsaug", "augment_addnoise", "augment_noise", "augment_pitch", "augment_randomsplice", "augment_silence", "augment_time", "augment_volume"]
default_audio_cleaners the default cleaning strategies used during audio modeling if clean_data == True ["clean_mono16hz"] ["clean_getfirst3secs", "clean_keyword", "clean_mono16hz", "clean_towav", "clean_multispeaker", "clean_normalizevolume", "clean_opus", "clean_randomsplice", "clean_removenoise", "clean_removesilence", "clean_rename", "clean_utterances"]
default_audio_features default set of audio features used for featurization (list). ["standard_features"] ["allosaurus_features", "audioset_features", "audiotext_features", "hubert_features", "librosa_features", "meta_features", "mixed_features", "opensmile_features", "pause_features", "praat_features", "prosody_features", "pspeech_features", "pyaudio_features", "pyaudiolex_features", "sa_features", "sox_features", "specimage_features", "specimage2_features", "spectrogram_features", "speechmetrics_features", "standard_features", "surfboard_features"]
default_audio_transcriber the default transcription model used during audio featurization if trainscribe_audio == True ["deepspeech_dict"] ["pocketsphinx", "deepspeech_nodict", "deepspeech_dict", "hubert", "google", "wit", "azure", "bing", "houndify", "ibm", "wav2vec"]
default_csv_augmenters the default augmentation strategies used to augment .CSV file types as part of model training if augment_data==True ["augment_ctgan_regression"] ["augment_ctgan_classification", "augment_ctgan_regression"]
default_csv_cleaners the default cleaning strategies used to clean .CSV file types as part of model training if clean_data==True ["clean_csv"] ["clean_csv"]
default_csv_features the default featurization technique(s) used as a part of model training for .CSV files. ["csv_features_regression"] ["csv_features_regression"]
default_csv_transcriber the default transcription technique for .CSV file spreadsheets. ["raw text"] ["raw text"]
default_dimensionality_reducer the default dimensionality reduction technique used if reduce_dimensions==True ["pca"] ["pca", "lda", "tsne", "plda","autoencoder"]
default_feature_selector the default feature selector used if select_features == True ["rfe"] ["chi", "fdr", "fpr", "fwe", "lasso", "percentile", "rfe", "univariate", "variance"]
default_image_augmenters the default augmentation techniques used for images if augment_data == True as a part of model training. ["augment_imgaug"] ["augment_imgaug"]
default_image_cleaners the default cleaning techniques used for image data as a part of model training is clean_data == True ["clean_greyscale"] ["clean_extractfaces", "clean_greyscale", "clean_jpg2png"]
default_image_features default set of image features used for featurization (list). ["image_features"] ["image_features", "inception_features", "resnet_features", "squeezenet_features", "tesseract_features", "vgg16_features", "vgg19_features", "xception_features"]
default_image_transcriber the default transcription technique used for images (e.g. image --> text transcript) ["tesseract"] ["tesseract"]
default_outlier_detector the default outlier technique(s) used to clean data as a part of model training if remove_outliers == True ["isolationforest"] ["isolationforest", "zscore"]
default_scaler the default scaling technique used to preprocess data during model training if scale_features == True ["standard_scaler"] ["binarizer", "one_hot_encoder", "normalize", "power_transformer", "poly", "quantile_transformer", "standard_scaler"]
default_text_augmenters the default augmentation strategies used during model training for text data if augment_data == True ["augment_textacy"] ["augment_textacy", "augment_summary"]
default_text_cleaners the default cleaning techniques used during model training on text data if clean_data == True ["clean_textacy"] ["clean_summary", "clean_textacy"]
default_text_features default set of text features used for featurization (list). ["nltk_features"] ["bert_features", "fast_features", "glove_features", "grammar_features", "nltk_features", "spacy_features", "text_features", "w2v_features"]
default_text_transcriber the default transcription techniques used to parse raw .TXT files during model training ["raw_text"] ["raw_text"]
default_training_script the specified traning script(s) to train machine learning models. Note that if you specify multiple training scripts here that the training scripts will be executed serially (list). ["tpot"] ["alphapy", "atm", "autogbt", "autokaggle", "autokeras", "auto-pytorch", "btb", "cvopt", "devol", "gama", "hyperband", "hypsklearn", "hungabunga", "imbalance-learn", "keras", "ludwig", "mlblocks", "neuraxle", "safe", "scsr", "tpot"]
default_video_augmenters the default augmentation strategies used for videos during model training if augment_data == True ["augment_vidaug"] ["augment_vidaug"]
default_video_cleaners the default cleaning strategies used for videos if clean_data == True ["clean_alignfaces"] ["clean_alignfaces", "clean_videostabilize"]
default_video_features default set of video features used for featurization (list). ["video_features"] ["video_features", "y8m_features"]
default_video_transcriber the default transcription technique used for videos (.mp4 --> text from the video) ["tesseract (averaged over frames)"] ["tesseract (averaged over frames)"]
dimension_number the number of dimensions to reduce a dataset into if reduce_dimensions == True 100 any integer from 1 to the number of features-1
feature_number the number of features to select for via the feature selection strategy (default_feature_selector) if select_features == True 20 any integer from 1 to the number of features-1
model_compress a setting that specifies whether or not to compress machine learning models during model training False True, False
reduce_dimensions a setting that specifies whether or not to reduce dimensions via the default_dimensionality_reducer False True, False
remove_outliers a setting that specifies whether or not to remove outliers during model training via the default_outlier_detector True True, False
scale_features a setting that specifies whether or not to scale features during featurization and model training via the default_scaler True True, False
select_features a setting that specifies whether or not to employ specified feature selection strategies (via the default_feature_selector) True True, False
test_size a setting that specifies the size of the testing dataset for defining model performance after model training. 0.10 Any number 0.10-0.50
transcribe_audio a setting to define whether or not to transcribe audio files during featurization and model training via the default_audio_transcriber True True, False
transcribe_csv a setting to define whether or not to transcribe csv files during featurization and model training via the default_csv_transcriber True True, False
transcribe_image a setting to define whether or not to transcribe image files during featurization and model training via the default_image_transcriber True True, False
transcribe_text a setting to define whether or not to transcribe text files during featurization and model training via the default_image_transcriber True True, False
transcribe_video a setting to define whether or not to transcribe video files during featurization and model training via the default_video_transcriber True True, False
visualize_data a setting to define whether or not to visualize features during the model training process via Allie's visualization API False True, False

License

This repository is licensed under an Apache 2.0 License.

The logo gif is created and owned by Tony Babel.

Feedback

Any feedback on this repository is greatly appreciated.

Contribute

We have built this machine learning framework to be quite agile to fit many purposes and needs, and we're excited to see how the open source community uses it. Forks and PRs are encouraged!

If you want to be a contributer, check out the active projects. There are many components of Allie that are expanding including the Annotation API, Visualization API, Features API, Modeling API, Cleaning API, and Augmentation API. If you are interested to contribute, please send me an email @ [email protected] specifying the project you'd like to be a part of and I'll plug you in with the right team.

Citation

Feel free to cite our work using:

Schwoebel, James. (2020). Allie [Computer software]. Retrieved from https://github.com/jim-schwoebel/allie

Additional resources

You may want to read through the wiki for additional documentation.


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