little fix

different subclasses of ``Featurizer`` for convenience:

### Performances

1. Index splitting

Index splitting

|Dataset    |N(tasks)	|N(samples) |Model               |Train score/ROC-AUC|Valid score/ROC-AUC|Time(loading)/s |Time(running)/s|

|-----------|-----------|-----------|--------------------|-------------------|-------------------|----------------|---------------| 

|tox21      |12         |8014       |logistic regression |0.910              |0.759              |30              |60             |

|           |           |           |tensorflow(MT-NN)	 |0.926        	     |0.705              |80              |2300           |

|           |           |           |graph convolution   |0.906        	     |0.725              |80              |900            |

2. Random splitting(N(tasks), N(samples), Time omitted)

Random splitting(N(tasks), N(samples), Time omitted)

|Dataset    |Model               |Train score/ROC-AUC|Valid score/ROC-AUC|

|-----------|--------------------|-------------------|-------------------|

|tox21      |logistic regression |0.903              |0.741              |

|           |robust MT-NN        |0.814              |0.692              |

|           |graph convolution   |0.820        	     |0.692              |

3. Scaffold splitting(N(tasks), N(samples), Time omitted)

Scaffold splitting(N(tasks), N(samples), Time omitted)

|Dataset    |Model               |Train score/ROC-AUC|Valid score/ROC-AUC|

|-----------|--------------------|-------------------|-------------------|

|tox21      |logistic regression |0.900              |0.650              |