Loading deepchem/data/tests/__init__.pydeleted 100644 → 0 +0 −122 Original line number Diff line number Diff line """ General API for testing dataset objects """ __author__ = "Bharath Ramsundar" __copyright__ = "Copyright 2016, Stanford University" __license__ = "MIT" import unittest import tempfile import os import shutil import numpy as np import deepchem as dc def load_solubility_data(): """Loads solubility dataset""" current_dir = os.path.dirname(os.path.abspath(__file__)) featurizer = dc.feat.CircularFingerprint(size=1024) tasks = ["log-solubility"] task_type = "regression" input_file = os.path.join(current_dir, "../../models/tests/example.csv") loader = dc.data.CSVLoader( tasks=tasks, smiles_field="smiles", featurizer=featurizer) return loader.featurize(input_file) def load_butina_data(): """Loads solubility dataset""" current_dir = os.path.dirname(os.path.abspath(__file__)) featurizer = dc.feat.CircularFingerprint(size=1024) tasks = ["task"] # task_type = "regression" input_file = os.path.join(current_dir, "../../models/tests/butina_example.csv") loader = dc.data.CSVLoader( tasks=tasks, smiles_field="smiles", featurizer=featurizer) return loader.featurize(input_file) def load_multitask_data(): """Load example multitask data.""" current_dir = os.path.dirname(os.path.abspath(__file__)) featurizer = dc.feat.CircularFingerprint(size=1024) tasks = [ "task0", "task1", "task2", "task3", "task4", "task5", "task6", "task7", "task8", "task9", "task10", "task11", "task12", "task13", "task14", "task15", "task16" ] input_file = os.path.join(current_dir, "../../models/tests/multitask_example.csv") loader = dc.data.CSVLoader( tasks=tasks, smiles_field="smiles", featurizer=featurizer) return loader.featurize(input_file) def load_classification_data(): """Loads classification data from example.csv""" current_dir = os.path.dirname(os.path.abspath(__file__)) featurizer = dc.feat.CircularFingerprint(size=1024) tasks = ["outcome"] task_type = "classification" input_file = os.path.join(current_dir, "../../models/tests/example_classification.csv") loader = dc.data.CSVLoader( tasks=tasks, smiles_field="smiles", featurizer=featurizer) return loader.featurize(input_file) def load_sparse_multitask_dataset(): """Load sparse tox multitask data, sample dataset.""" current_dir = os.path.dirname(os.path.abspath(__file__)) featurizer = dc.feat.CircularFingerprint(size=1024) tasks = [ "task1", "task2", "task3", "task4", "task5", "task6", "task7", "task8", "task9" ] input_file = os.path.join(current_dir, "../../models/tests/sparse_multitask_example.csv") loader = dc.data.CSVLoader( tasks=tasks, smiles_field="smiles", featurizer=featurizer) return loader.featurize(input_file) def load_feat_multitask_data(): """Load example with numerical features, tasks.""" current_dir = os.path.dirname(os.path.abspath(__file__)) features = ["feat0", "feat1", "feat2", "feat3", "feat4", "feat5"] featurizer = dc.feat.UserDefinedFeaturizer(features) tasks = ["task0", "task1", "task2", "task3", "task4", "task5"] input_file = os.path.join(current_dir, "../../models/tests/feat_multitask_example.csv") loader = dc.data.UserCSVLoader( tasks=tasks, featurizer=featurizer, id_field="id") return loader.featurize(input_file) def load_gaussian_cdf_data(): """Load example with numbers sampled from Gaussian normal distribution. Each feature and task is a column of values that is sampled from a normal distribution of mean 0, stdev 1.""" current_dir = os.path.dirname(os.path.abspath(__file__)) features = ["feat0", "feat1"] featurizer = dc.feat.UserDefinedFeaturizer(features) tasks = ["task0", "task1"] input_file = os.path.join(current_dir, "../../models/tests/gaussian_cdf_example.csv") loader = dc.data.UserCSVLoader( tasks=tasks, featurizer=featurizer, id_field="id") return loader.featurize(input_file) def load_unlabelled_data(): current_dir = os.path.dirname(os.path.abspath(__file__)) featurizer = dc.feat.CircularFingerprint(size=1024) tasks = [] input_file = os.path.join(current_dir, "../../data/tests/no_labels.csv") loader = dc.data.CSVLoader( tasks=tasks, smiles_field="smiles", featurizer=featurizer) return loader.featurize(input_file) Loading
deepchem/data/tests/__init__.pydeleted 100644 → 0 +0 −122 Original line number Diff line number Diff line """ General API for testing dataset objects """ __author__ = "Bharath Ramsundar" __copyright__ = "Copyright 2016, Stanford University" __license__ = "MIT" import unittest import tempfile import os import shutil import numpy as np import deepchem as dc def load_solubility_data(): """Loads solubility dataset""" current_dir = os.path.dirname(os.path.abspath(__file__)) featurizer = dc.feat.CircularFingerprint(size=1024) tasks = ["log-solubility"] task_type = "regression" input_file = os.path.join(current_dir, "../../models/tests/example.csv") loader = dc.data.CSVLoader( tasks=tasks, smiles_field="smiles", featurizer=featurizer) return loader.featurize(input_file) def load_butina_data(): """Loads solubility dataset""" current_dir = os.path.dirname(os.path.abspath(__file__)) featurizer = dc.feat.CircularFingerprint(size=1024) tasks = ["task"] # task_type = "regression" input_file = os.path.join(current_dir, "../../models/tests/butina_example.csv") loader = dc.data.CSVLoader( tasks=tasks, smiles_field="smiles", featurizer=featurizer) return loader.featurize(input_file) def load_multitask_data(): """Load example multitask data.""" current_dir = os.path.dirname(os.path.abspath(__file__)) featurizer = dc.feat.CircularFingerprint(size=1024) tasks = [ "task0", "task1", "task2", "task3", "task4", "task5", "task6", "task7", "task8", "task9", "task10", "task11", "task12", "task13", "task14", "task15", "task16" ] input_file = os.path.join(current_dir, "../../models/tests/multitask_example.csv") loader = dc.data.CSVLoader( tasks=tasks, smiles_field="smiles", featurizer=featurizer) return loader.featurize(input_file) def load_classification_data(): """Loads classification data from example.csv""" current_dir = os.path.dirname(os.path.abspath(__file__)) featurizer = dc.feat.CircularFingerprint(size=1024) tasks = ["outcome"] task_type = "classification" input_file = os.path.join(current_dir, "../../models/tests/example_classification.csv") loader = dc.data.CSVLoader( tasks=tasks, smiles_field="smiles", featurizer=featurizer) return loader.featurize(input_file) def load_sparse_multitask_dataset(): """Load sparse tox multitask data, sample dataset.""" current_dir = os.path.dirname(os.path.abspath(__file__)) featurizer = dc.feat.CircularFingerprint(size=1024) tasks = [ "task1", "task2", "task3", "task4", "task5", "task6", "task7", "task8", "task9" ] input_file = os.path.join(current_dir, "../../models/tests/sparse_multitask_example.csv") loader = dc.data.CSVLoader( tasks=tasks, smiles_field="smiles", featurizer=featurizer) return loader.featurize(input_file) def load_feat_multitask_data(): """Load example with numerical features, tasks.""" current_dir = os.path.dirname(os.path.abspath(__file__)) features = ["feat0", "feat1", "feat2", "feat3", "feat4", "feat5"] featurizer = dc.feat.UserDefinedFeaturizer(features) tasks = ["task0", "task1", "task2", "task3", "task4", "task5"] input_file = os.path.join(current_dir, "../../models/tests/feat_multitask_example.csv") loader = dc.data.UserCSVLoader( tasks=tasks, featurizer=featurizer, id_field="id") return loader.featurize(input_file) def load_gaussian_cdf_data(): """Load example with numbers sampled from Gaussian normal distribution. Each feature and task is a column of values that is sampled from a normal distribution of mean 0, stdev 1.""" current_dir = os.path.dirname(os.path.abspath(__file__)) features = ["feat0", "feat1"] featurizer = dc.feat.UserDefinedFeaturizer(features) tasks = ["task0", "task1"] input_file = os.path.join(current_dir, "../../models/tests/gaussian_cdf_example.csv") loader = dc.data.UserCSVLoader( tasks=tasks, featurizer=featurizer, id_field="id") return loader.featurize(input_file) def load_unlabelled_data(): current_dir = os.path.dirname(os.path.abspath(__file__)) featurizer = dc.feat.CircularFingerprint(size=1024) tasks = [] input_file = os.path.join(current_dir, "../../data/tests/no_labels.csv") loader = dc.data.CSVLoader( tasks=tasks, smiles_field="smiles", featurizer=featurizer) return loader.featurize(input_file)
