"""
Test reload for trained models.
"""
from __future__ import print_function
from __future__ import division
from __future__ import unicode_literals

__author__ = "Bharath Ramsundar"
__copyright__ = "Copyright 2016, Stanford University"
__license__ = "GPL"

import unittest
import tempfile
import numpy as np
import deepchem as dc
import tensorflow as tf
from sklearn.ensemble import RandomForestClassifier

class TestReload(unittest.TestCase):

  def test_sklearn_reload(self):
    """Test that trained model can be reloaded correctly."""
    n_samples = 10
    n_features = 3
    n_tasks = 1 
    
    # Generate dummy dataset
    np.random.seed(123)
    ids = np.arange(n_samples)
    X = np.random.rand(n_samples, n_features)
    y = np.random.randint(2, size=(n_samples, n_tasks))
    w = np.ones((n_samples, n_tasks))
  
    dataset = dc.data.NumpyDataset(X, y, w, ids)
    classification_metric = dc.metrics.Metric(dc.metrics.roc_auc_score)

    sklearn_model = RandomForestClassifier()
    model_dir = tempfile.mkdtemp()
    model = dc.models.SklearnModel(sklearn_model, model_dir)

    # Fit trained model
    model.fit(dataset)
    model.save()

    # Load trained model
    reloaded_model = dc.models.SklearnModel(None, model_dir)
    reloaded_model.reload()

    # Eval model on train
    scores = reloaded_model.evaluate(dataset, [classification_metric])
    assert scores[classification_metric.name] > .9

  def test_tf_reload(self):
    """Test that tensorflow models can overfit simple classification datasets."""
    n_samples = 10
    n_features = 3
    n_tasks = 1 
    n_classes = 2
    
    # Generate dummy dataset
    np.random.seed(123)
    ids = np.arange(n_samples)
    X = np.random.rand(n_samples, n_features)
    y = np.random.randint(n_classes, size=(n_samples, n_tasks))
    w = np.ones((n_samples, n_tasks))
  
    dataset = dc.data.NumpyDataset(X, y, w, ids)

    classification_metric = dc.metrics.Metric(dc.metrics.accuracy_score)

    model_dir = tempfile.mkdtemp()
    model = dc.models.TensorflowMultiTaskClassifier(
          n_tasks, n_features, model_dir, dropouts=[0.])

    # Fit trained model
    model.fit(dataset)
    model.save()

    # Load trained model
    reloaded_model = dc.models.TensorflowMultiTaskClassifier(
        n_tasks, n_features, model_dir, dropouts=[0.])
    reloaded_model.reload()

    # Eval model on train
    scores = reloaded_model.evaluate(dataset, [classification_metric])
    assert scores[classification_metric.name] > .6
