def load_datasets(dataset_name, save_path='data/', url=None):
if dataset_name == 'synthetic':
gene_dataset = SyntheticDataset()
elif dataset_name == 'cortex':
gene_dataset = CortexDataset()
elif dataset_name == 'brain_large':
gene_dataset = BrainLargeDataset(save_path=save_path)
elif dataset_name == 'retina':
gene_dataset = RetinaDataset(save_path=save_path)
elif dataset_name == 'cbmc':
gene_dataset = CbmcDataset(save_path=save_path)
elif dataset_name == 'brain_small':
gene_dataset = BrainSmallDataset(save_path=save_path)
elif dataset_name == 'hemato':
gene_dataset = HematoDataset(save_path='data/HEMATO/')
elif dataset_name == 'pbmc':
gene_dataset = PbmcDataset(save_path=save_path)
elif dataset_name[-5:] == ".loom":
gene_dataset = LoomDataset(filename=dataset_name,
save_path=save_path,
url=url)
elif dataset_name[-5:] == ".h5ad":
gene_dataset = AnnDataset(dataset_name, save_path=save_path, url=url)
elif ".csv" in dataset_name:
gene_dataset = CsvDataset(dataset_name, save_path=save_path)
else:
raise "No such dataset available"
return gene_dataset
def load_datasets(dataset_name, save_path="data/", url=None):
if dataset_name == "synthetic":
gene_dataset = SyntheticDataset()
elif dataset_name == "cortex":
gene_dataset = CortexDataset()
elif dataset_name == "brain_large":
gene_dataset = BrainLargeDataset(save_path=save_path)
elif dataset_name == "retina":
gene_dataset = RetinaDataset(save_path=save_path)
elif dataset_name == "cbmc":
gene_dataset = CbmcDataset(save_path=save_path)
elif dataset_name == "brain_small":
gene_dataset = BrainSmallDataset(save_path=save_path)
elif dataset_name == "hemato":
gene_dataset = HematoDataset(save_path="data/HEMATO/")
elif dataset_name == "pbmc":
gene_dataset = PbmcDataset(save_path=save_path)
elif dataset_name[-5:] == ".loom":
gene_dataset = LoomDataset(filename=dataset_name, save_path=save_path, url=url)
elif dataset_name[-5:] == ".h5ad":
gene_dataset = AnnDataset(dataset_name, save_path=save_path, url=url)
elif ".csv" in dataset_name:
gene_dataset = CsvDataset(dataset_name, save_path=save_path)
else:
raise Exception("No such dataset available")
return gene_dataset
def test_retina():
retina_dataset = RetinaDataset(save_path='tests/data/')
base_benchmark(retina_dataset)
def test_retina(save_path):
retina_dataset = RetinaDataset(save_path=save_path)
base_benchmark(retina_dataset)
def test_retina_load_train_one(self):
dataset = RetinaDataset(save_path="tests/data")
unsupervised_training_one_epoch(dataset)
datasets = {
'scvi_pbmc': PbmcDataset(),
'bermuda_pbmc': CsvDataset(
str(DIRPATH / './pbmc/expression.csv'),
labels_file = str(DIRPATH / './pbmc/labels.csv'),
batch_ids_file = str(DIRPATH / './pbmc/batches.csv'),
gene_by_cell = False
),
'mouse': CsvDataset(
str(DIRPATH / './mouse_genes/ST1 - original_expression.csv'),
labels_file = str(DIRPATH / './mouse_genes/labels.csv'),
batch_ids_file = str(DIRPATH / './mouse_genes/batches.csv'),
gene_by_cell = False
),
#'pancreas': BermudaDataset('./pancreas/muraro_seurat.csv'),
'retina': RetinaDataset(),
'starmap': PreFrontalCortexStarmapDataset(),
}
parser = argparse.ArgumentParser(description="A way to define variables for \
training, tests and models")
parser.add_argument('--metrics_dir', type=str, help="Path to save metrics file \
\nDisabled if save_metrics flag is False.\n\nDefault value \
is './'")
parser.add_argument('--save_metrics', type=bool, help='Boolean flag determines \
whether metrics should be saved.\n\n Default value is True')
parser.add_argument('--custom_config', type=str, help='The path to the \
configuration file to be used. If equal to None, then \
the config located in the same folder will be used.',
default=None)
args = parser.parse_args()
import os
import numpy as np
import pandas as pd
from sklearn.manifold import TSNE
import matplotlib.pyplot as plt
from scvi.dataset import CortexDataset, RetinaDataset
from scvi.models import *
from scvi.inference import UnsupervisedTrainer
import torch
## Correction for batch effects
gene_dataset = RetinaDataset(save_path=save_path)
n_epochs=50 if n_epochs_all is None else n_epochs_all
lr=1e-3
use_batches=True
use_cuda=True
### Train the model and output model likelihood every 5 epochs
vae = VAE(gene_dataset.nb_genes, n_batch=gene_dataset.n_batches * use_batches)
trainer = UnsupervisedTrainer(vae,
gene_dataset,
train_size=0.9,
use_cuda=use_cuda,
frequency=5)
trainer.train(n_epochs=n_epochs, lr=lr)
#%%
# Plotting the likelihood change across the 50 epochs of training: blue for training error and orange for testing error.
