rm -r examples/sampl

"""

Script that trains graph-conv models on SAMPL(FreeSolv) dataset.

"""

from __future__ import print_function

from __future__ import division

from __future__ import unicode_literals

import numpy as np

np.random.seed(123)

import tensorflow as tf

tf.random.set_seed(123)

import deepchem as dc

# Load SAMPL(FreeSolv) dataset

SAMPL_tasks, SAMPL_datasets, transformers = dc.molnet.load_sampl(

    featurizer='GraphConv')

train_dataset, valid_dataset, test_dataset = SAMPL_datasets

# Define metric

metric = dc.metrics.Metric(dc.metrics.pearson_r2_score, np.mean)

# Batch size of models

batch_size = 50

model = dc.models.GraphConvModel(len(SAMPL_tasks), mode='regression')

# Fit trained model

model.fit(train_dataset, nb_epoch=20)

print("Evaluating model")

train_scores = model.evaluate(train_dataset, [metric], transformers)

valid_scores = model.evaluate(valid_dataset, [metric], transformers)

print("Train scores")

print(train_scores)

print("Validation scores")

print(valid_scores)

"""

Script that trains multitask models on SAMPL dataset.

"""

from __future__ import print_function

from __future__ import division

from __future__ import unicode_literals

import os

import shutil

import numpy as np

import deepchem as dc

from deepchem.molnet import load_sampl

# Only for debug!

np.random.seed(123)

# Load SAMPL dataset

n_features = 1024

SAMPL_tasks, SAMPL_datasets, transformers = load_sampl()

train_dataset, valid_dataset, test_dataset = SAMPL_datasets

# Fit models

metric = dc.metrics.Metric(dc.metrics.pearson_r2_score, np.mean)

model = dc.models.MultitaskRegressor(

    len(SAMPL_tasks),

    n_features,

    layer_sizes=[1000],

    dropouts=[.25],

    learning_rate=0.001,

    batch_size=50)

# Fit trained model

model.fit(train_dataset)

print("Evaluating model")

train_scores = model.evaluate(train_dataset, [metric], transformers)

valid_scores = model.evaluate(valid_dataset, [metric], transformers)

print("Train scores")

print(train_scores)

print("Validation scores")

print(valid_scores)