3.3. Data visualization
This is a univeral visualizer for all types of data as a part of model training.
Note that this is a setting in the Allie Framework (e.g. "visualize_data": true).
To get started, you first need to featurize some data using featurizations scripts. This data must be in the train_dir folder in the form of directories. To read more about featurization, see this page.
After you have featurized your data, go to this current folder (./allie/visualize') and run the visualize.py script:
python3 visualize.py [problemtype] [folder A] [folder B] ... [folder N]
Note you need to pass through the problem_type (e.g. 'audio'|'text'|'image'|'video'|'csv') and also all the relevant folders featurizations. In this case, we are looking at audio files separating males from females (e.g. featurizations exist) in the train_dir folder.
python3 visualize.py audio males females
This then generates a tree structure of graphs, for example below:
├── classes.png
├── clustering
│ ├── isomap.png
│ ├── lle.png
│ ├── mds.png
│ ├── modified.png
│ ├── pca.png
│ ├── spectral.png
│ ├── tsne.png
│ └── umap.png
├── feature_ranking
│ ├── correlation.png
│ ├── data.csv
│ ├── feature_importance.png
│ ├── feature_plots
│ │ ├── 0_F0semitoneFrom27.5Hz_sma3nz_amean.png
│ │ ├── 10_loudness_sma3_amean.png
│ │ ├── 11_loudness_sma3_stddevNorm.png
│ │ ├── 12_loudness_sma3_percentile20.0.png
│ │ ├── 13_loudness_sma3_percentile50.0.png
│ │ ├── 14_loudness_sma3_percentile80.0.png
│ │ ├── 15_loudness_sma3_pctlrange0-2.png
│ │ ├── 16_loudness_sma3_meanRisingSlope.png
│ │ ├── 17_loudness_sma3_stddevRisingSlope.png
│ │ ├── 18_loudness_sma3_meanFallingSlope.png
│ │ ├── 19_loudness_sma3_stddevFallingSlope.png
│ │ ├── 1_F0semitoneFrom27.5Hz_sma3nz_stddevNorm.png
│ │ ├── 2_F0semitoneFrom27.5Hz_sma3nz_percentile20.0.png
│ │ ├── 3_F0semitoneFrom27.5Hz_sma3nz_percentile50.0.png
│ │ ├── 4_F0semitoneFrom27.5Hz_sma3nz_percentile80.0.png
│ │ ├── 5_F0semitoneFrom27.5Hz_sma3nz_pctlrange0-2.png
│ │ ├── 6_F0semitoneFrom27.5Hz_sma3nz_meanRisingSlope.png
│ │ ├── 7_F0semitoneFrom27.5Hz_sma3nz_stddevRisingSlope.png
│ │ ├── 8_F0semitoneFrom27.5Hz_sma3nz_meanFallingSlope.png
│ │ ├── 9_F0semitoneFrom27.5Hz_sma3nz_stddevFallingSlope.png
│ │ └── feature_plots_transformed
│ │ ├── 0_F0semitoneFrom27.5Hz_sma3nz_amean.png
│ │ ├── 10_loudness_sma3_amean.png
│ │ ├── 11_loudness_sma3_stddevNorm.png
│ │ ├── 12_loudness_sma3_percentile20.0.png
│ │ ├── 13_loudness_sma3_percentile50.0.png
│ │ ├── 14_loudness_sma3_percentile80.0.png
│ │ ├── 15_loudness_sma3_pctlrange0-2.png
│ │ ├── 16_loudness_sma3_meanRisingSlope.png
│ │ ├── 17_loudness_sma3_stddevRisingSlope.png
│ │ ├── 18_loudness_sma3_meanFallingSlope.png
│ │ ├── 19_loudness_sma3_stddevFallingSlope.png
│ │ ├── 1_F0semitoneFrom27.5Hz_sma3nz_stddevNorm.png
│ │ ├── 2_F0semitoneFrom27.5Hz_sma3nz_percentile20.0.png
│ │ ├── 3_F0semitoneFrom27.5Hz_sma3nz_percentile50.0.png
│ │ ├── 4_F0semitoneFrom27.5Hz_sma3nz_percentile80.0.png
│ │ ├── 5_F0semitoneFrom27.5Hz_sma3nz_pctlrange0-2.png
│ │ ├── 6_F0semitoneFrom27.5Hz_sma3nz_meanRisingSlope.png
│ │ ├── 7_F0semitoneFrom27.5Hz_sma3nz_stddevRisingSlope.png
│ │ ├── 8_F0semitoneFrom27.5Hz_sma3nz_meanFallingSlope.png
│ │ └── 9_F0semitoneFrom27.5Hz_sma3nz_stddevFallingSlope.png
│ ├── heatmap.png
│ ├── heatmap_clean.png
│ ├── lasso.png
│ ├── pearson.png
│ ├── radviz.png
│ └── shapiro.png
└── model_selection
├── calibration.png
├── cluster_distance.png
├── elbow.png
├── ks.png
├── learning_curve.png
├── logr_percentile_plot.png
├── outliers.png
├── pca_explained_variance.png
├── precision-recall.png
├── prediction_error.png
├── residuals.png
├── roc_curve.png
├── roc_curve_train.png
├── siloutte.png
└── thresholds.png
Once you run this script, you output many visualizations. These visualizations can be customized within the script itself with some simple modifications. See below for some of the visualizations you can make.
Note that this script considers whether or not to balance datasets (e.g. "balance_data": true in settings.json) - so make sure you adjust your settings as to whether or not you'd like to balance the data before running the script above. These were the settings used to create the visualizations below:
{
"version": "1.0.0",
"augment_data": false,
"balance_data": true,
"clean_data": false,
"create_YAML": true,
"create_csv": true,
"default_audio_features": [ "opensmile_features" ],
"default_audio_transcriber": ["deepspeech_dict"],
"default_csv_features": [ "csv_features" ],
"default_csv_transcriber": ["raw text"],
"default_dimensionality_reducer": [ "pca" ],
"default_feature_selector": [ "lasso" ],
"default_image_features": [ "image_features" ],
"default_image_transcriber": ["tesseract"],
"default_scaler": [ "standard_scaler" ],
"default_text_features": [ "nltk_features" ],
"default_text_transcriber": "raw text",
"default_training_script": [ "tpot" ],
"default_video_features": [ "video_features" ],
"default_video_transcriber": [ "tesseract (averaged over frames)" ],
"feature_number": 20,
"model_compress": false,
"reduce_dimensions": false,
"scale_features": true,
"select_features": false,
"test_size": 0.10,
"transcribe_audio": false,
"transcribe_csv": true,
"transcribe_image": true,
"transcribe_text": true,
"transcribe_video": true,
"visualize_data": true
}
You can also view the raw data in the visualization session.
Quickly iterate and see which cluster method works best with your dataset.
├── clustering
│ ├── isomap.png
│ ├── lle.png
│ ├── mds.png
│ ├── modified.png
│ ├── pca.png
│ ├── spectral.png
│ ├── tsne.png
| └── umap.png
├── feature_ranking
│ ├── feature_importance.png
│ ├── feature_plots
│ │ └── 128_mfcc_10_std.png
... [all feature plots (many files)]
│ ├── heatmap.png
│ ├── heatmap_clean.png
│ ├── lasso.png
│ ├── pearson.png
│ └── shapiro.png
Easily plots the top 20 features via violin plots (to spot distributions).
Heatmap with correlated variables
└── model_selection
├── calibration.png
├── cluster_distance.png
├── elbow.png
├── ks.png
├── learning_curve.png
├── logr_percentile_plot.png
├── outliers.png
├── pca_explained_variance.png
├── precision-recall.png
├── prediction_error.png
├── residuals.png
├── roc_curve.png
├── roc_curve_train.png
├── siloutte.png
└── thresholds.png
