Python load_hopv Exemples

Exemple #1

"""
Script that trains graph-conv models on HOPV 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.set_random_seed(123)
import deepchem as dc
from hopv_datasets import load_hopv

# Load HOPV dataset
hopv_tasks, hopv_datasets, transformers = load_hopv(featurizer='GraphConv')
train_dataset, valid_dataset, test_dataset = hopv_datasets

# Fit models
metric = [
    dc.metrics.Metric(dc.metrics.pearson_r2_score, np.mean, mode="regression"),
    dc.metrics.Metric(dc.metrics.mean_absolute_error,
                      np.mean,
                      mode="regression")
]

# Number of features on conv-mols
n_feat = 75
# Batch size of models
batch_size = 50
graph_model = dc.nn.SequentialGraph(n_feat)

Exemple #2

Fichier : hopv_tf_models.py Projet : swayam01/schrodingerdeepchem

Script that trains multitask models on HOPV dataset.
"""
from __future__ import print_function
from __future__ import division
from __future__ import unicode_literals

import numpy as np
import deepchem as dc
from hopv_datasets import load_hopv

# Only for debug!
np.random.seed(123)

# Load HOPV dataset
n_features = 1024
hopv_tasks, hopv_datasets, transformers = load_hopv()
train_dataset, valid_dataset, test_dataset = hopv_datasets

# Fit models
metric = [
    dc.metrics.Metric(dc.metrics.pearson_r2_score, np.mean, mode="regression"),
    dc.metrics.Metric(
        dc.metrics.mean_absolute_error, np.mean, mode="regression")
]

model = dc.models.TensorflowMultiTaskRegressor(
    len(hopv_tasks),
    n_features,
    layer_sizes=[1000],
    dropouts=[.25],
    learning_rate=0.001,

Exemple #3

Fichier : hopv_graph_conv.py Projet : joegomes/deepchem

"""
Script that trains graph-conv models on HOPV 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.set_random_seed(123)
import deepchem as dc
from hopv_datasets import load_hopv

# Load HOPV dataset
hopv_tasks, hopv_datasets, transformers = load_hopv(featurizer='GraphConv')
train_dataset, valid_dataset, test_dataset = hopv_datasets

# Fit models
metric = [
    dc.metrics.Metric(dc.metrics.pearson_r2_score, np.mean, mode="regression"),
    dc.metrics.Metric(
        dc.metrics.mean_absolute_error, np.mean, mode="regression")
]

# Number of features on conv-mols
n_feat = 75
# Batch size of models
batch_size = 50
graph_model = dc.nn.SequentialGraph(n_feat)
graph_model.add(dc.nn.GraphConv(64, n_feat, activation='relu'))