changes

import deepchem as dc

import numpy as np

import sklearn

from sklearn.ensemble import RandomForestClassifier

N = 10

N = 100

n_feat = 5

n_classes = 3

n_tasks = 1

X = np.random.rand(N, n_feat)

y = np.random.randint(3, size=(N, n_tasks))

y = np.random.randint(3, size=(N,))

dataset = dc.data.NumpyDataset(X, y)

sklearn_model = RandomForestClassifier(

    class_weight="balanced", n_estimators=50)

model = dc.models.SklearnModel(sklearn_model)

# Fit models

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

# Fit trained model

print("About to fit model")

model.fit(dataset)

model.save()

print("About to evaluate model")

train_scores = model.evaluate(dataset, [metric], [])

train_scores = model.evaluate(dataset,

    sklearn.metrics.roc_auc_score, [])

print("Train scores")

print(train_scores)