I designed this library as part of my Ph.D. project, which is based on TensorFlow and Scikit-Learn toolboxes. With approximately a thousand lines of Python code, it allows quick testing of the latest machine learning technologies to make a predictive model of synapses, and test the predictive power of models.
| module | tested version |
|---|---|
| Python | 3.7.6 |
| jupyter | 1.0.0 |
| tensorflow | 2.3 |
| tensorflow-addons | 0.11.1 |
| scikit-learn | 0.22.1 |
| eli5 | 0.10.1 |
| psutil | 5.6.7 |
| matplotlib | 3.1.0 |
| pandas | 1.0.1 |
| scipy | 1.4.1 |
| numpy | 1.18.5 |
| plotly | 4.9.0 |
| plotly-orca | 1.3.0 |
On Linux "xvfb" package (Virtual Framebuffer 'fake' X server) may be needed as well for plotly to function.
Check Readme.ipynb for a quick start guide.
The main function is assess_model.
| function parameter | default value | description |
|---|---|---|
| train_features_filename | None | data features: optional number of columns. If tau_r, tau_f, and U in the target matrix are NA, then inter-stimulus interval (ISI) should be set to zero. |
| train_targets_filename | None | data targets: five synaptic parameters g, tau_d, tau_r, tau_f, and U. NA values should be replaced with zero already. |
| predict_data_startswith | None | Any number of files containing prediction features. The columns of features should be identical to the training features. Each row should be one potential connection. |
| potential_connections_df | None | A DataFrame containing the list of potential connections. Each row is linked to prediction feature data files. |
| numerical_columns | ['Slice_Thickness', 'ISI', 'Temperature', 'Vm', 'Erev_GABA_B', 'Erev_NMDA', 'Erev_GABA_A', 'Erev_AMPA', 'Cai', 'Cao', 'Cli', 'Clo', 'Csi', 'H2PO4o', 'HCO3i', 'HCO3o', 'HEPESi', 'Ki', 'Ko', 'Mgi', 'Mgo', 'Nai', 'Nao', 'Bri', 'gluconatei', 'QX314i', 'ATPi', 'EGTAi', 'EGTAo', 'GTPi', 'OHi', 'SO4i', 'SO4o', 'phosphocreatinei', 'methanesulfonatei', 'acetatei', 'methylsulfatei', 'NMDGi', 'Trisi', 'CeSO4i', 'pyruvateo', 'TEAi', 'Bao', 'HPO4o', 'Age'] | List of feature columns that need normalization. |
| passage_num | 1 | the number of times NA values are replaced with the interpolations. Is used just for naming the folders for convenience. |
| complete_stp | True | interpolate the NA values by changing zero ISIs to the mode of the non-zero ISIs in the training data. |
| isi_column_name | 'ISI' | the name of feature column that contains ISI values. |
| validation_data | None | Data that is sent to Keras during training to test the model on every epoch. |
| jack_knife | False | Tests the model with the jack-knife method. Each time you run the model one row is set aside and the model is trained using the remaining data. The results are stored in a folder. counter.txt file keeps track of the row number that is processed already. |
| random_forest | False | If set to True a random forest model is made instead of deep learning. |
| matrix_plot_is_needed | False | If True, a matrix plot of the five synaptic parameters is being plotted for each of the predictions. |
| feature_importance_iterations | 0 | If an integer more than zero is being used, a feature importance bar chart is being plotted. |
| source_directory | None | You can save trained models in a folder and ask the program to load the models in the source folder and retrained them. |
| destination_directory | None | you can choose a custom folder to save the results. |
| template_model | None | a model is being loaded and being used as a template. |
| template_weights | None | only weights of an earlier model are being loaded. |
| num_nodes | [8192, 512, 128, 512, 8192] | You can define the number of nodes in each layer of your deep learning model (a multilayer perceptron). |
| activation | 'mish' | activation function of nodes. Supported functions include: 'tanh', 'softsign', 'softmax', 'PReLU', 'ReLU', 'LeakyReLU', 'ELU', 'selu', 'swish', 'mish', 'gelu', 'lisht', 'isrlu', and 'relu'. |
| drop_out | [0.5, 0.5, 0.05, 0.5, 0.5] | dropout rate after each layer of the neural net. |
| batch_normalization | True | Enables batch_normalization. Works better if large batch sizes are used. |
| noise | 0.2 | By default adds 20% Gaussian noise after the input layer and noise/4=5% after the output layer. |
| L1_weight | None | L1 regularization weight. |
| L2_weight | 1e-3 | L2 regularization weight. |
| min_weight | None | minimum allowed weight between nodes of the network. |
| max_weight | 1.0 | maximum allowed weight between nodes of the network. |
| batch_size | 2621 | Training batch size. Using large batch_sizes leads to a faster training speed. The prediction power of the models trained with large batch sizes was also better in the case of synapses. |
| num_epochs | 9999 | maximum number of training epochs after which training ends. |
| repetitions | 4 | number of times convergence of the model is being tested by checking if the model has reached the goal values also by checking the bio-plausibility of the predictions |
| optimizer | 'ADAMW' | The name of the optimizer. Supported methods: 'ADAMW', 'SGDW', 'ADAM', and 'SGD'. The default method worked the best in our tests. |
| lookahead | True | A secondary optimizer that controls the training. Slightly improved the results in our tests. |
| learning_rate | 0.015 | Learning rate of the optimizer. Large values speed up the training. |
| LROPpatience | 100 | If training did not improve after this number of epochs reduce the learning rate by a factor. |
| LROPfactor | 0.9 | Learning rate reduction factor. The new learning_rate = previous learning_rate times LROPfactor. |
| loss | 'SMAPE' | The loss function that computes the model error. Supported functions: 'MAPE' (mean absolute percentage error), 'MLAPE' (mean log1p absolute percentage error), 'MSLAPE' (mean squared log1p absolute percentage error), 'SMAAPE' (mean arctan absolute percentage error), 'ML1APE' (mean soft_l1 absolute percentage error), and 'MSLE' (mean squared logarithmic error). |
| loss_threshold | 29 | Loss threshold after which bio-plausibility is being tested. |
| loss_goal | 29 | Loss goal after which training is being stopped. |
| negative_penalty_coef | 0.0 | experimentally penalizes negative predictions if MSLE is being used as a loss function and the output layer activation is non-sigmoid. |
| patience | 500 | number of epochs training continues after finding the minimum loss value that is bellow threshold as well. |
| maape_threshold | 27 | accuracy threshold like the loss that is tested with MAAPE function. |
| maape_goal | 27 | accuracy goal like loss goal |
| ob_threshold | 100 | how many out of bound predictions are allowed. |
| skip | 200 | skip epochs while plotting the training curves. |
| never_stop | False | used while retraining models from a source folder. If no other model remains in the folder continue checking the folder. |
| save_models | False | save the trained model at the end. |
| verbose | 0 | if set to one, more training logs would be generated. |