If you’re new to DeepChem, you probably want to know the basics. What is DeepChem? Why should you care about using it? The short answer is that DeepChem is a scientific machine learning library. (The “Chem” indicates the historical fact that DeepChem initially focused on chemical applications, but we aim to support all types of scientific applications more broadly).
Why would you want to use DeepChem instead of another machine learning library? Simply put, DeepChem maintains an extensive collection of utilities to enable scientific deep learning including classes for loading scientific datasets, processing them, transforming them, splitting them up, and learning from them. Behind the scenes DeepChem uses a variety of other machine learning frameworks such as scikit-learn, TensorFlow, and XGBoost. We are also experimenting with adding additional models implemented in PyTorch and JAX. Our focus is to facilitate scientific experimentation using whatever tools are available at hand.
In the rest of this tutorials, we’ll provide a rapid fire overview of DeepChem’s API. DeepChem is a big library so we won’t cover everything, but we should give you enough to get started.
dc.data module contains utilities to handle
Dataset objects are the heart of DeepChem.
Dataset is an abstraction of a dataset in machine learning. That is,
a collection of features, labels, weights, alongside associated identifiers.
Rather than explaining further, we’ll just show you.
>>> import deepchem as dc >>> import numpy as np >>> N_samples = 50 >>> n_features = 10 >>> X = np.random.rand(N_samples, n_features) >>> y = np.random.rand(N_samples) >>> dataset = dc.data.NumpyDataset(X, y) >>> dataset.X.shape (50, 10) >>> dataset.y.shape (50,)
Here we’ve used the
NumpyDataset class which stores datasets in memory.
This works fine for smaller datasets and is very convenient for experimentation,
but is less convenient for larger datasets. For that we have the
>>> dataset = dc.data.DiskDataset.from_numpy(X, y) >>> dataset.X.shape (50, 10) >>> dataset.y.shape (50,)
In this example we haven’t specified a data directory, so this
DiskDataset is written
to a temporary folder. Note that
dataset.y load data
from disk underneath the hood! So this can get very expensive for larger datasets.
“Featurizer” is a chunk of code which transforms raw input data into a processed
form suitable for machine learning. The
dc.feat module contains an extensive collection
of featurizers for molecules, molecular complexes and inorganic crystals.
We’ll show you the example about the usage of featurizers.
>>> smiles = [ ... 'O=Cc1ccc(O)c(OC)c1', ... 'CN1CCC[C@H]1c2cccnc2', ... 'C1CCCCC1', ... 'c1ccccc1', ... 'CC(=O)O', ... ] >>> properties = [0.4, -1.5, 3.2, -0.2, 1.7] >>> featurizer = dc.feat.CircularFingerprint(size=1024) >>> ecfp = featurizer.featurize(smiles) >>> ecfp.shape (5, 1024) >>> dataset = dc.data.NumpyDataset(X=ecfp, y=np.array(properties)) >>> len(dataset) 5
Here, we’ve used the
CircularFingerprint and converted SMILES to ECFP.
The ECFP is a fingerprint which is a bit vector made by chemical structure information
and we can use it as the input for various models.
And then, you may have a CSV file which contains SMILES and property like HOMO-LUMO gap.
In such a case, by using
DataLoader, you can load and featurize your data at once.
>>> import pandas as pd >>> # make a dataframe object for creating a CSV file >>> df = pd.DataFrame(list(zip(smiles, properties)), columns=["SMILES", "property"]) >>> import tempfile >>> with dc.utils.UniversalNamedTemporaryFile(mode='w') as tmpfile: ... # dump the CSV file ... df.to_csv(tmpfile.name) ... # initizalize the featurizer ... featurizer = dc.feat.CircularFingerprint(size=1024) ... # initizalize the dataloader ... loader = dc.data.CSVLoader(["property"], feature_field="SMILES", featurizer=featurizer) ... # load and featurize the data from the CSV file ... dataset = loader.create_dataset(tmpfile.name) ... len(dataset) 5
dc.splits module contains a collection of scientifically aware splitters.
Generally, we need to split the original data to training, validation and test data
in order to tune the model and evaluate the model’s performance.
We’ll show you the example about the usage of splitters.
>>> splitter = dc.splits.RandomSplitter() >>> # split 5 datapoints in the ratio of train:valid:test = 3:1:1 >>> train_dataset, valid_dataset, test_dataset = splitter.train_valid_test_split( ... dataset=dataset, frac_train=0.6, frac_valid=0.2, frac_test=0.2 ... ) >>> len(train_dataset) 3 >>> len(valid_dataset) 1 >>> len(test_dataset) 1
Here, we’ve used the
RandomSplitter and splitted the data randomly
in the ratio of train:valid:test = 3:1:1. But, the random splitting sometimes
overestimates model’s performance, especially for small data or imbalance data.
Please be careful for model evaluation. The
dc.splits provides more methods
and algorithms to evaluate the model’s performance appropriately, like cross validation or
splitting using molecular scaffolds.
dc.models contains an extensive collection of models for scientific applications.
Most of all models inherits
dc.models.Model and we can train them by just calling
You don’t need to care about how to use specific framework APIs.
We’ll show you the example about the usage of models.
>>> from sklearn.ensemble import RandomForestRegressor >>> rf = RandomForestRegressor() >>> model = dc.models.SklearnModel(model=rf) >>> # model training >>> model.fit(train_dataset) >>> valid_preds = model.predict(valid_dataset) >>> valid_preds.shape (1,) >>> test_preds = model.predict(test_dataset) >>> test_preds.shape (1,)
Here, we’ve used the
SklearnModel and trained the model.
Even if you want to train a deep learning model which is implemented
by TensorFlow or PyTorch, calling
fit method is all you need!
And then, if you use
dc.metrics.Metric, you can evaluate your model
by just calling
>>> # initialze the metric >>> metric = dc.metrics.Metric(dc.metrics.mae_score) >>> # evaluate the model >>> train_score = model.evaluate(train_dataset, [metric]) >>> valid_score = model.evaluate(valid_dataset, [metric]) >>> test_score = model.evaluate(test_dataset, [metric])