Loading deepchem/metrics/__init__.py +11 −9 Original line number Diff line number Diff line Loading @@ -90,7 +90,7 @@ class Metric(object): """Wrapper class for computing user-defined metrics.""" def __init__(self, metric, task_averager=None, name=None, threshold=None, verbosity=None, mode="classification"): verbosity=None, mode=None): """ Args: metric: function that takes args y_true, y_pred (in that order) and Loading @@ -111,6 +111,15 @@ class Metric(object): self.name = name self.verbosity = verbosity self.threshold = threshold if mode is None: if self.name in ["roc_auc_score", "matthews_corrcoef", "recall_score", "accuracy_score", "kappa_score"]: mode = "classification" elif self.name in ["r2_score", "mean_squared_error", "mean_absolute_error"]: mode = "regression" else: raise ValueError("Must specify mode for new metric.") assert mode in ["classification", "regression"] self.mode = mode Loading Loading @@ -163,19 +172,12 @@ class Metric(object): Raises: NotImplementedError: If metric_str is not in METRICS. """ print("compute_singletask_metric") print("y_true") print(y_true) print("y_pred") print(y_pred) print("w") print(w) y_true = y_true[w != 0] y_pred = y_pred[w != 0] # If there are no nonzero examples, metric is ill-defined. if not len(y_true): return np.nan if self.mode == "classification": if self.name == "roc_auc_score": y_true = to_one_hot(y_true).astype(int) y_pred = y_pred[:, np.newaxis] if self.threshold is not None: Loading deepchem/models/__init__.py +15 −1 Original line number Diff line number Diff line Loading @@ -131,6 +131,9 @@ class Model(object): def predict(self, dataset, transformers): """ Uses self to make predictions on provided Dataset object. Returns: y_pred: numpy ndarray of shape (n_samples,) """ y_preds = [] batch_size = self.model_params["batch_size"] Loading @@ -146,10 +149,21 @@ class Model(object): return y_pred def predict_proba(self, dataset, transformers): """ TODO: Do transformers even make sense here? Returns: y_pred: numpy ndarray of shape (n_samples, n_classes*n_tasks) """ y_preds = [] batch_size = self.model_params["batch_size"] n_classes = None for (X_batch, y_batch, w_batch, ids_batch) in dataset.iterbatches(batch_size): y_pred_batch = np.reshape(self.predict_proba_on_batch(X_batch), y_batch.shape) y_pred_batch = self.predict_proba_on_batch(X_batch) if n_classes is None: n_classes = y_pred_batch.shape[1] batch_size = len(y_batch) y_pred_batch = np.reshape(y_pred_batch, (batch_size, n_classes)) y_pred_batch = undo_transforms(y_pred_batch, transformers) y_preds.append(y_pred_batch) y_pred = np.vstack(y_preds) Loading deepchem/models/sklearn_models/__init__.py +0 −5 Original line number Diff line number Diff line Loading @@ -43,11 +43,6 @@ class SklearnModel(Model): """ X, y, w, _ = dataset.to_numpy() y, w = y.flatten(), w.flatten() print("fit") print("X") print(X) print("y") print(y) self.raw_model.fit(X, y, w) y_pred_raw = self.raw_model.predict(X) Loading deepchem/models/tensorflow_models/__init__.py +1 −1 Original line number Diff line number Diff line Loading @@ -658,7 +658,7 @@ class TensorflowRegressor(TensorflowGraph): A tensor with shape batch_size containing the weighted cost for each example. """ return tf.mul(0.5 * tf.square(output - labels), weights) #return tf.mul(0.5 * tf.square(output - labels), weights) def example_counts(self, y_true): """Get counts of examples in each class. Loading deepchem/models/tensorflow_models/fcnet.py +162 −0 Original line number Diff line number Diff line Loading @@ -57,11 +57,13 @@ from __future__ import print_function from __future__ import division from __future__ import unicode_literals import time import numpy as np import tensorflow as tf from tensorflow.python.platform import logging from deepchem.models.tensorflow_models import TensorflowClassifier from deepchem.models.tensorflow_models import TensorflowRegressor from deepchem.models.tensorflow_models import model_ops from deepchem.metrics import to_one_hot Loading Loading @@ -146,3 +148,163 @@ class TensorflowMultiTaskClassifier(TensorflowClassifier): orig_dict["valid"] = np.ones((self.model_params["batch_size"],), dtype=bool) return self._get_feed_dict(orig_dict) class TensorflowMultiTaskRegressor(TensorflowRegressor): """Implements an icml model as configured in a model_config.proto.""" def build(self): """Constructs the graph architecture as specified in its config. This method creates the following Placeholders: mol_features: Molecule descriptor (e.g. fingerprint) tensor with shape batch_size x num_features. """ assert len(self.model_params["data_shape"]) == 1 num_features = self.model_params["data_shape"][0] with self.graph.as_default(): with tf.name_scope(self.placeholder_scope): self.mol_features = tf.placeholder( tf.float32, shape=[self.model_params["batch_size"], num_features], name='mol_features') layer_sizes = self.model_params["layer_sizes"] weight_init_stddevs = self.model_params["weight_init_stddevs"] bias_init_consts = self.model_params["bias_init_consts"] dropouts = self.model_params["dropouts"] lengths_set = { len(layer_sizes), len(weight_init_stddevs), len(bias_init_consts), len(dropouts), } assert len(lengths_set) == 1, 'All layer params must have same length.' num_layers = lengths_set.pop() assert num_layers > 0, 'Must have some layers defined.' prev_layer = self.mol_features prev_layer_size = num_features for i in xrange(num_layers): layer = tf.nn.relu(model_ops.FullyConnectedLayer( tensor=prev_layer, size=layer_sizes[i], weight_init=tf.truncated_normal( shape=[prev_layer_size, layer_sizes[i]], stddev=weight_init_stddevs[i]), bias_init=tf.constant(value=bias_init_consts[i], shape=[layer_sizes[i]]))) layer = model_ops.Dropout(layer, dropouts[i]) prev_layer = layer prev_layer_size = layer_sizes[i] self.output = [tf.squeeze(model_ops.FullyConnectedLayer( tensor=prev_layer, size=layer_sizes[i], weight_init=tf.truncated_normal( shape=[prev_layer_size, 1], stddev=weight_init_stddevs[i]), bias_init=tf.constant(value=bias_init_consts[i], shape=[1])))] def construct_feed_dict(self, X_b, y_b=None, w_b=None, ids_b=None): """Construct a feed dictionary from minibatch data. TODO(rbharath): ids_b is not used here. Can we remove it? Args: X_b: np.ndarray of shape (batch_size, num_features) y_b: np.ndarray of shape (batch_size, num_tasks) w_b: np.ndarray of shape (batch_size, num_tasks) ids_b: List of length (batch_size) with datapoint identifiers. """ orig_dict = {} orig_dict["mol_features"] = X_b for task in xrange(self.num_tasks): if y_b is not None: orig_dict["labels_%d" % task] = y_b[:, task] else: # Dummy placeholders orig_dict["labels_%d" % task] = np.squeeze( np.zeros((self.model_params["batch_size"],))) if w_b is not None: orig_dict["weights_%d" % task] = w_b[:, task] else: # Dummy placeholders orig_dict["weights_%d" % task] = np.ones( (self.model_params["batch_size"],)) orig_dict["valid"] = np.ones((self.model_params["batch_size"],), dtype=bool) return self._get_feed_dict(orig_dict) def predict_on_batch(self, X): """Return model output for the provided input. Restore(checkpoint) must have previously been called on this object. Args: dataset: deepchem.datasets.dataset object. Returns: Tuple of three numpy arrays with shape num_examples x num_tasks (x ...): output: Model outputs. labels: True labels. weights: Example weights. Note that the output and labels arrays may be more than 2D, e.g. for classifier models that return class probabilities. Raises: AssertionError: If model is not in evaluation mode. ValueError: If output and labels are not both 3D or both 2D. """ if not self._restored_model: self.restore() with self.graph.as_default(): assert not model_ops.is_training() self.require_attributes(['output', 'labels', 'weights']) # run eval data through the model num_tasks = self.num_tasks output, labels, weights = [], [], [] start = time.time() with self._get_shared_session().as_default(): batch_count = -1.0 feed_dict = self.construct_feed_dict(X) batch_start = time.time() batch_count += 1 data = self._get_shared_session().run( self.output + self.labels + self.weights, feed_dict=feed_dict) batch_output = np.asarray(data[:num_tasks], dtype=float) batch_labels = np.asarray(data[num_tasks:num_tasks * 2], dtype=float) batch_weights = np.asarray(data[num_tasks * 2:num_tasks * 3], dtype=float) # reshape to batch_size x num_tasks x ... if batch_output.ndim == 3 and batch_labels.ndim == 3: batch_output = batch_output.transpose((1, 0, 2)) batch_labels = batch_labels.transpose((1, 0, 2)) elif batch_output.ndim == 2 and batch_labels.ndim == 2: batch_output = batch_output.transpose((1, 0)) batch_labels = batch_labels.transpose((1, 0)) else: raise ValueError( 'Unrecognized rank combination for output and labels: %s %s' % (batch_output.shape, batch_labels.shape)) batch_weights = batch_weights.transpose((1, 0)) valid = feed_dict[self.valid.name] # only take valid outputs if np.count_nonzero(~valid): batch_output = batch_output[valid] batch_labels = batch_labels[valid] batch_weights = batch_weights[valid] output.append(batch_output) labels.append(batch_labels) weights.append(batch_weights) logging.info('Eval batch took %g seconds', time.time() - start) #labels = np.array(from_one_hot( # np.squeeze(np.concatenate(labels)), axis=-1)) labels = np.squeeze(np.concatenate(labels)) return np.copy(labels) Loading
deepchem/metrics/__init__.py +11 −9 Original line number Diff line number Diff line Loading @@ -90,7 +90,7 @@ class Metric(object): """Wrapper class for computing user-defined metrics.""" def __init__(self, metric, task_averager=None, name=None, threshold=None, verbosity=None, mode="classification"): verbosity=None, mode=None): """ Args: metric: function that takes args y_true, y_pred (in that order) and Loading @@ -111,6 +111,15 @@ class Metric(object): self.name = name self.verbosity = verbosity self.threshold = threshold if mode is None: if self.name in ["roc_auc_score", "matthews_corrcoef", "recall_score", "accuracy_score", "kappa_score"]: mode = "classification" elif self.name in ["r2_score", "mean_squared_error", "mean_absolute_error"]: mode = "regression" else: raise ValueError("Must specify mode for new metric.") assert mode in ["classification", "regression"] self.mode = mode Loading Loading @@ -163,19 +172,12 @@ class Metric(object): Raises: NotImplementedError: If metric_str is not in METRICS. """ print("compute_singletask_metric") print("y_true") print(y_true) print("y_pred") print(y_pred) print("w") print(w) y_true = y_true[w != 0] y_pred = y_pred[w != 0] # If there are no nonzero examples, metric is ill-defined. if not len(y_true): return np.nan if self.mode == "classification": if self.name == "roc_auc_score": y_true = to_one_hot(y_true).astype(int) y_pred = y_pred[:, np.newaxis] if self.threshold is not None: Loading
deepchem/models/__init__.py +15 −1 Original line number Diff line number Diff line Loading @@ -131,6 +131,9 @@ class Model(object): def predict(self, dataset, transformers): """ Uses self to make predictions on provided Dataset object. Returns: y_pred: numpy ndarray of shape (n_samples,) """ y_preds = [] batch_size = self.model_params["batch_size"] Loading @@ -146,10 +149,21 @@ class Model(object): return y_pred def predict_proba(self, dataset, transformers): """ TODO: Do transformers even make sense here? Returns: y_pred: numpy ndarray of shape (n_samples, n_classes*n_tasks) """ y_preds = [] batch_size = self.model_params["batch_size"] n_classes = None for (X_batch, y_batch, w_batch, ids_batch) in dataset.iterbatches(batch_size): y_pred_batch = np.reshape(self.predict_proba_on_batch(X_batch), y_batch.shape) y_pred_batch = self.predict_proba_on_batch(X_batch) if n_classes is None: n_classes = y_pred_batch.shape[1] batch_size = len(y_batch) y_pred_batch = np.reshape(y_pred_batch, (batch_size, n_classes)) y_pred_batch = undo_transforms(y_pred_batch, transformers) y_preds.append(y_pred_batch) y_pred = np.vstack(y_preds) Loading
deepchem/models/sklearn_models/__init__.py +0 −5 Original line number Diff line number Diff line Loading @@ -43,11 +43,6 @@ class SklearnModel(Model): """ X, y, w, _ = dataset.to_numpy() y, w = y.flatten(), w.flatten() print("fit") print("X") print(X) print("y") print(y) self.raw_model.fit(X, y, w) y_pred_raw = self.raw_model.predict(X) Loading
deepchem/models/tensorflow_models/__init__.py +1 −1 Original line number Diff line number Diff line Loading @@ -658,7 +658,7 @@ class TensorflowRegressor(TensorflowGraph): A tensor with shape batch_size containing the weighted cost for each example. """ return tf.mul(0.5 * tf.square(output - labels), weights) #return tf.mul(0.5 * tf.square(output - labels), weights) def example_counts(self, y_true): """Get counts of examples in each class. Loading
deepchem/models/tensorflow_models/fcnet.py +162 −0 Original line number Diff line number Diff line Loading @@ -57,11 +57,13 @@ from __future__ import print_function from __future__ import division from __future__ import unicode_literals import time import numpy as np import tensorflow as tf from tensorflow.python.platform import logging from deepchem.models.tensorflow_models import TensorflowClassifier from deepchem.models.tensorflow_models import TensorflowRegressor from deepchem.models.tensorflow_models import model_ops from deepchem.metrics import to_one_hot Loading Loading @@ -146,3 +148,163 @@ class TensorflowMultiTaskClassifier(TensorflowClassifier): orig_dict["valid"] = np.ones((self.model_params["batch_size"],), dtype=bool) return self._get_feed_dict(orig_dict) class TensorflowMultiTaskRegressor(TensorflowRegressor): """Implements an icml model as configured in a model_config.proto.""" def build(self): """Constructs the graph architecture as specified in its config. This method creates the following Placeholders: mol_features: Molecule descriptor (e.g. fingerprint) tensor with shape batch_size x num_features. """ assert len(self.model_params["data_shape"]) == 1 num_features = self.model_params["data_shape"][0] with self.graph.as_default(): with tf.name_scope(self.placeholder_scope): self.mol_features = tf.placeholder( tf.float32, shape=[self.model_params["batch_size"], num_features], name='mol_features') layer_sizes = self.model_params["layer_sizes"] weight_init_stddevs = self.model_params["weight_init_stddevs"] bias_init_consts = self.model_params["bias_init_consts"] dropouts = self.model_params["dropouts"] lengths_set = { len(layer_sizes), len(weight_init_stddevs), len(bias_init_consts), len(dropouts), } assert len(lengths_set) == 1, 'All layer params must have same length.' num_layers = lengths_set.pop() assert num_layers > 0, 'Must have some layers defined.' prev_layer = self.mol_features prev_layer_size = num_features for i in xrange(num_layers): layer = tf.nn.relu(model_ops.FullyConnectedLayer( tensor=prev_layer, size=layer_sizes[i], weight_init=tf.truncated_normal( shape=[prev_layer_size, layer_sizes[i]], stddev=weight_init_stddevs[i]), bias_init=tf.constant(value=bias_init_consts[i], shape=[layer_sizes[i]]))) layer = model_ops.Dropout(layer, dropouts[i]) prev_layer = layer prev_layer_size = layer_sizes[i] self.output = [tf.squeeze(model_ops.FullyConnectedLayer( tensor=prev_layer, size=layer_sizes[i], weight_init=tf.truncated_normal( shape=[prev_layer_size, 1], stddev=weight_init_stddevs[i]), bias_init=tf.constant(value=bias_init_consts[i], shape=[1])))] def construct_feed_dict(self, X_b, y_b=None, w_b=None, ids_b=None): """Construct a feed dictionary from minibatch data. TODO(rbharath): ids_b is not used here. Can we remove it? Args: X_b: np.ndarray of shape (batch_size, num_features) y_b: np.ndarray of shape (batch_size, num_tasks) w_b: np.ndarray of shape (batch_size, num_tasks) ids_b: List of length (batch_size) with datapoint identifiers. """ orig_dict = {} orig_dict["mol_features"] = X_b for task in xrange(self.num_tasks): if y_b is not None: orig_dict["labels_%d" % task] = y_b[:, task] else: # Dummy placeholders orig_dict["labels_%d" % task] = np.squeeze( np.zeros((self.model_params["batch_size"],))) if w_b is not None: orig_dict["weights_%d" % task] = w_b[:, task] else: # Dummy placeholders orig_dict["weights_%d" % task] = np.ones( (self.model_params["batch_size"],)) orig_dict["valid"] = np.ones((self.model_params["batch_size"],), dtype=bool) return self._get_feed_dict(orig_dict) def predict_on_batch(self, X): """Return model output for the provided input. Restore(checkpoint) must have previously been called on this object. Args: dataset: deepchem.datasets.dataset object. Returns: Tuple of three numpy arrays with shape num_examples x num_tasks (x ...): output: Model outputs. labels: True labels. weights: Example weights. Note that the output and labels arrays may be more than 2D, e.g. for classifier models that return class probabilities. Raises: AssertionError: If model is not in evaluation mode. ValueError: If output and labels are not both 3D or both 2D. """ if not self._restored_model: self.restore() with self.graph.as_default(): assert not model_ops.is_training() self.require_attributes(['output', 'labels', 'weights']) # run eval data through the model num_tasks = self.num_tasks output, labels, weights = [], [], [] start = time.time() with self._get_shared_session().as_default(): batch_count = -1.0 feed_dict = self.construct_feed_dict(X) batch_start = time.time() batch_count += 1 data = self._get_shared_session().run( self.output + self.labels + self.weights, feed_dict=feed_dict) batch_output = np.asarray(data[:num_tasks], dtype=float) batch_labels = np.asarray(data[num_tasks:num_tasks * 2], dtype=float) batch_weights = np.asarray(data[num_tasks * 2:num_tasks * 3], dtype=float) # reshape to batch_size x num_tasks x ... if batch_output.ndim == 3 and batch_labels.ndim == 3: batch_output = batch_output.transpose((1, 0, 2)) batch_labels = batch_labels.transpose((1, 0, 2)) elif batch_output.ndim == 2 and batch_labels.ndim == 2: batch_output = batch_output.transpose((1, 0)) batch_labels = batch_labels.transpose((1, 0)) else: raise ValueError( 'Unrecognized rank combination for output and labels: %s %s' % (batch_output.shape, batch_labels.shape)) batch_weights = batch_weights.transpose((1, 0)) valid = feed_dict[self.valid.name] # only take valid outputs if np.count_nonzero(~valid): batch_output = batch_output[valid] batch_labels = batch_labels[valid] batch_weights = batch_weights[valid] output.append(batch_output) labels.append(batch_labels) weights.append(batch_weights) logging.info('Eval batch took %g seconds', time.time() - start) #labels = np.array(from_one_hot( # np.squeeze(np.concatenate(labels)), axis=-1)) labels = np.squeeze(np.concatenate(labels)) return np.copy(labels)
