Fix examples

import tensorflow as tf

tf.random.set_seed(123)

import deepchem as dc

import sklearn

# Load delaney dataset

delaney_tasks, delaney_datasets, transformers = dc.molnet.load_delaney()

train, valid, test = delaney_datasets

# Fit models

regression_metric = dc.metrics.Metric(dc.metrics.pearson_r2_score)

metric = dc.metrics.Metric(dc.metrics.pearson_r2_score)

def rf_model_builder(**model_params):

  rf_params = {k: v for (k, v) in model_params.items() if k != 'model_dir'}

  sklearn_model = sklearn.ensemble.RandomForestRegressor(**rf_params)

  return dc.models.SklearnModel(sklearn_model, model_dir)

params_dict = {"n_estimators": 30}

optimizer = dc.hyper.GaussianProcessHyperparamOpt(rf_model_builder)

best_hyper_params, best_performance = optimizer.hyperparam_search(

    params_dict,

    train_dataset,

    valid_dataset,

    transformers,

    metric)

best_model, best_params, all_results = optimizer.hyperparam_search(

    params_dict, train, valid, transformers, metric)

import numpy as np

np.random.seed(123)

import tensorflow as tf

tf.random.set_seed(123)

import deepchem as dc

import sklearn

# Load delaney dataset

delaney_tasks, delaney_datasets, transformers = dc.molnet.load_delaney()

train, valid, test = delaney_datasets

# Fit models

metric = dc.metrics.Metric(dc.metrics.pearson_r2_score)

def rf_model_builder(**model_params):

  rf_params = {k: v for (k, v) in model_params.items() if k != 'model_dir'}

  model_dir = model_params['model_dir']

  sklearn_model = sklearn.ensemble.RandomForestRegressor(**rf_params)

  return dc.models.SklearnModel(sklearn_model, model_dir)

params_dict = {"n_estimators": [10, 30, 50, 100]}

optimizer = dc.hyper.GridHyperparamOpt(rf_model_builder)

best_model, best_params, all_results = optimizer.hyperparam_search(

    params_dict, train, valid, transformers, metric)