MathiasGruber

def prediction2label(pred: np.ndarray):
    """Convert ordinal predictions to class labels, e.g.
    
    [0.9, 0.1, 0.1, 0.1] -> 0
    [0.9, 0.9, 0.1, 0.1] -> 1
    [0.9, 0.9, 0.9, 0.1] -> 2
    etc.
    """
    return (pred > 0.5).cumprod(axis=1).sum(axis=1) - 1

MathiasGruber

def ordinal_regression(predictions: List[List[float]], targets: List[float]):
    """Ordinal regression with encoding as in https://arxiv.org/pdf/0704.1028.pdf"""

    # Create out modified target with [batch_size, num_labels] shape
    modified_target = torch.zeros_like(predictions)

    # Fill in ordinal target function, i.e. 0 -> [1,0,0,...]
    for i, target in enumerate(targets):
        modified_target[i, 0:target+1] = 1

MathiasGruber

def cross_entropy(predictions: List[List[float]], targets: List[float]):
    return nn.CrossEntropyLoss(reduction='none')(predictions, target)

MathiasGruber

python train.py \
    --data_path data/solubility_dataset.csv \
    --dataset_type multiclass \
    --save_dir solubility_checkpoints/ \
    --ensemble 1 --num_folds 5 --epochs 50 \
    --split_type scaffold_balanced \
    --multiclass_num_classes 5 \
    --save_smiles_splits --save_preds

MathiasGruber

# Train a logSolubility model
python train.py \
    --data_path data/delaney.csv \
    --dataset_type regression \
    --save_dir delaney_checkpoints \
    --ensemble 3 --num_folds 10 --epochs 50

# Get bayes ensemble grad results
python interpret_local.py \
    --test_path data/delaney_subset.csv \

MathiasGruber

chemprop_predict \
    --test_path data/clintox_test.csv \
    --checkpoint_dir model_checkpoint \
    --features_generator rdkit_2d_normalized --no_features_scaling \
    --preds_path data/predictions.csv

MathiasGruber

chemprop_train \
    --data_path data/clintox_train.csv \
    --config_path data/config.json \
    --dataset_type classification \
    --save_dir model_checkpoint \
    --num_folds 5 \
    --ensemble_size 3 \
    --features_generator rdkit_2d_normalized --no_features_scaling \
    --split_type scaffold_balanced

MathiasGruber

chemprop_hyperopt \
    --data_path data/clintox_train.csv \
    --dataset_type classification \
    --num_iters 50 \
    --features_generator rdkit_2d_normalized --no_features_scaling \
    --config_save_path data/config.json

MathiasGruber

apiVersion: v1
kind: Service
metadata:
  labels:
    app: [VAR_MODULE_NAME]
  name: [VAR_MODULE_NAME]
spec:
  ports:
    - name: predict
      port: 5000

MathiasGruber

import os

def deploy_bentoml(model_name):
    """Deploys a BentoML from dockerhub into Kubernetes cluster"""

    # Open a template for BentoML deployments
    with open('bentoml_deploy.tpl', 'r') as fi:

        # Substitute the name of the model into template & save
        yaml_file = fi.read()