tox_model = SingletaskToMultitask(tox_tasks,
tox_task_types,
params_dict,
tox_model_dir,
model_builder,
verbosity=verbosity)
tox_model.reload()
"""
Load sider models now
"""
base_sider_data_dir = "/home/apappu/deepchem-models/toxcast_models/sider/sider_data"
sider_tasks, sider_dataset, sider_transformers = load_sider(
base_sider_data_dir, reload=reload)
base_sider_dir = "/home/apappu/deepchem-models/toxcast_models/sider/sider_analysis"
sider_train_dir = os.path.join(base_sider_dir, "train_dataset")
sider_valid_dir = os.path.join(base_sider_dir, "valid_dataset")
sider_test_dir = os.path.join(base_sider_dir, "test_dataset")
sider_model_dir = os.path.join(base_sider_dir, "model")
sider_splitter = RandomSplitter()
sider_train_dataset, sider_valid_dataset, sider_test_dataset = sider_splitter.train_valid_test_split(
sider_dataset, sider_train_dir, sider_valid_dir, sider_test_dir)
# Fit Logistic Regression models
sider_task_types = {task: "classification" for task in sider_tasks}
"""
Script that trains Sklearn multitask models on the sider dataset
@Author Bharath Ramsundar, Aneesh Pappu
"""
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 sider_datasets import load_sider
from sklearn.ensemble import RandomForestClassifier
sider_tasks, datasets, transformers = load_sider()
train_dataset, valid_dataset, test_dataset = datasets
metric = dc.metrics.Metric(dc.metrics.roc_auc_score,
np.mean,
mode="classification")
def model_builder(model_dir):
sklearn_model = RandomForestClassifier(class_weight="balanced",
n_estimators=100)
return dc.models.SklearnModel(sklearn_model, model_dir)
model = dc.models.SingletaskToMultitask(sider_tasks, model_builder)
"""
Script that trains Sklearn multitask models on the sider dataset
@Author Bharath Ramsundar, Aneesh Pappu
"""
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 sider_datasets import load_sider
from sklearn.ensemble import RandomForestClassifier
sider_tasks, datasets, transformers = load_sider()
train_dataset, valid_dataset, test_dataset = datasets
metric = dc.metrics.Metric(dc.metrics.roc_auc_score, np.mean, mode="classification")
def model_builder(model_dir):
sklearn_model = RandomForestClassifier(class_weight="balanced", n_estimators=100)
return dc.models.SklearnModel(sklearn_model, model_dir)
model = dc.models.SingletaskToMultitask(sider_tasks, model_builder)
# Fit trained model
model.fit(train_dataset)
model.save()
from deepchem.datasets import Dataset
from deepchem import metrics
from deepchem.metrics import Metric
from deepchem.utils.evaluate import Evaluator
from deepchem.models.keras_models.fcnet import MultiTaskDNN
from deepchem.models.keras_models import KerasModel
# Set some global variables up top
np.random.seed(123)
reload = True
verbosity = "high"
model = "logistic"
base_data_dir = "/tmp/sider_keras"
sider_tasks, dataset, transformers = load_sider(
base_data_dir, reload=reload)
print("len(dataset)")
print(len(dataset))
base_dir = "/tmp/sider_analysis"
model_dir = os.path.join(base_dir, "model")
if os.path.exists(base_dir):
shutil.rmtree(base_dir)
os.makedirs(base_dir)
# Load SIDER data
sider_tasks, sider_datasets, transformers = load_sider(
base_dir, reload=reload)
train_dataset, valid_dataset = sider_datasets
n_features = 1024
