from __future__ import print_function
from __future__ import division
from __future__ import unicode_literals

import numpy as np

from models import GraphConvTensorGraph

np.random.seed(123)
import tensorflow as tf
tf.set_random_seed(123)
import deepchem as dc
from deepchem.molnet 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
model = GraphConvTensorGraph(
    len(hopv_tasks), batch_size=batch_size, mode='regression')

# Fit trained model
model.fit(train_dataset, nb_epoch=25)

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)

from __future__ import print_function
from __future__ import division
from __future__ import unicode_literals

import numpy as np

from models import GraphConvModel

np.random.seed(123)
import tensorflow as tf
tf.set_random_seed(123)
import deepchem as dc
from deepchem.molnet 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
model = GraphConvModel(
    len(hopv_tasks), batch_size=batch_size, mode='regression')

# Fit trained model
model.fit(train_dataset, nb_epoch=25)

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)

Fix GraphConvTensorGraph to GraphConvModel in hopv example
Fix GraphConvTensorGraph to GraphConvModel in hopv example

  from __future__ import print_function
  from __future__ import division
  from __future__ import unicode_literals
  
  import numpy as np
  
- from models import GraphConvTensorGraph
+ from models import GraphConvModel
  
  np.random.seed(123)
  import tensorflow as tf
  tf.set_random_seed(123)
  import deepchem as dc
  from deepchem.molnet 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
- model = GraphConvTensorGraph(
+ model = GraphConvModel(
      len(hopv_tasks), batch_size=batch_size, mode='regression')
  
  # Fit trained model
  model.fit(train_dataset, nb_epoch=25)
  
  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)
  
Fix GraphConvTensorGraph to GraphConvModel in hopv example
## Code Before:
from __future__ import print_function
from __future__ import division
from __future__ import unicode_literals

import numpy as np

from models import GraphConvTensorGraph

np.random.seed(123)
import tensorflow as tf
tf.set_random_seed(123)
import deepchem as dc
from deepchem.molnet 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
model = GraphConvTensorGraph(
    len(hopv_tasks), batch_size=batch_size, mode='regression')

# Fit trained model
model.fit(train_dataset, nb_epoch=25)

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)

## Instruction:
Fix GraphConvTensorGraph to GraphConvModel in hopv example
## Code After:
from __future__ import print_function
from __future__ import division
from __future__ import unicode_literals

import numpy as np

from models import GraphConvModel

np.random.seed(123)
import tensorflow as tf
tf.set_random_seed(123)
import deepchem as dc
from deepchem.molnet 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
model = GraphConvModel(
    len(hopv_tasks), batch_size=batch_size, mode='regression')

# Fit trained model
model.fit(train_dataset, nb_epoch=25)

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)
