How to use get_download_dir method in autotest

Best Python code snippet using autotest_python

load_datasets.py

Source:load_datasets.py

...10                 log_every=1000,11                 cache_file_path='./bbbp_dglgraph.bin',12                 n_jobs=1):13        self._url = 'dataset/bbbp.zip'14        data_path = get_download_dir() + '/bbbp.zip'15        dir_path = get_download_dir() + '/bbbp'16        download(_get_dgl_url(self._url), path=data_path, overwrite=False)17        extract_archive(data_path, dir_path)18        df = pd.read_csv(dir_path + '/BBBP.csv')19        super(BBBP, self).__init__(df=df,20                                   smiles_to_graph=smiles_to_graph,21                                   smiles_column='smiles',22                                   cache_file_path=cache_file_path,23                                   task_names=['p_np'],24                                   load=load,25                                   log_every=log_every,26                                   init_mask=True,27                                   n_jobs=n_jobs)28        self.load_full = False29        self.names = df['name'].tolist()30        self.names = [self.names[i] for i in self.valid_ids]31    def __getitem__(self, item):32        if self.load_full:33            return self.smiles[item], self.graphs[item], self.labels[item], \34                   self.mask[item], self.names[item]35        else:36            return self.smiles[item], self.graphs[item], self.labels[item], self.mask[item]37class BACE(MoleculeCSVDataset):38    def __init__(self,39                 smiles_to_graph=smiles_2_dgl,40                 load=False,41                 log_every=1000,42                 cache_file_path='./bace_dglgraph.bin',43                 n_jobs=1):44        self._url = 'dataset/bace.zip'45        data_path = get_download_dir() + '/bace.zip'46        dir_path = get_download_dir() + '/bace'47        download(_get_dgl_url(self._url), path=data_path, overwrite=False)48        extract_archive(data_path, dir_path)49        df = pd.read_csv(dir_path + '/bace.csv')50        super(BACE, self).__init__(df=df,51                                   smiles_to_graph=smiles_to_graph,52                                   smiles_column='mol',53                                   cache_file_path=cache_file_path,54                                   task_names=['Class'],55                                   load=load,56                                   log_every=log_every,57                                   init_mask=True,58                                   n_jobs=n_jobs)59        self.load_full = False60        self.ids = df['CID'].tolist()61        self.ids = [self.ids[i] for i in self.valid_ids]62    def __getitem__(self, item):63        if self.load_full:64            return self.smiles[item], self.graphs[item], self.labels[item], \65                   self.mask[item], self.ids[item]66        else:67            return self.smiles[item], self.graphs[item], self.labels[item], self.mask[item]68class MUV(MoleculeCSVDataset):69    def __init__(self,70                 smiles_to_graph=smiles_2_dgl,71                 load=False,72                 log_every=1000,73                 cache_file_path='./muv_dglgraph.bin',74                 n_jobs=1):75        self._url = 'dataset/muv.zip'76        data_path = get_download_dir() + '/muv.zip'77        dir_path = get_download_dir() + '/muv'78        download(_get_dgl_url(self._url), path=data_path, overwrite=False)79        extract_archive(data_path, dir_path)80        df = pd.read_csv(dir_path + '/muv.csv')81        self.ids = df['mol_id'].tolist()82        self.load_full = False83        df = df.drop(columns=['mol_id'])84        super(MUV, self).__init__(df=df,85                                  smiles_to_graph=smiles_to_graph,86                                  smiles_column='smiles',87                                  cache_file_path=cache_file_path,88                                  load=load,89                                  log_every=log_every,90                                  init_mask=True,91                                  n_jobs=n_jobs)92        self.ids = [self.ids[i] for i in self.valid_ids]93    def __getitem__(self, item):94        if self.load_full:95            return self.smiles[item], self.graphs[item], self.labels[item], \96                   self.mask[item], self.ids[item]97        else:98            return self.smiles[item], self.graphs[item], self.labels[item], self.mask[item]99class ClinTox(MoleculeCSVDataset):100    def __init__(self,101                 smiles_to_graph=smiles_2_dgl,102                 load=False,103                 log_every=1000,104                 cache_file_path='./clintox_dglgraph.bin',105                 n_jobs=1):106        self._url = 'dataset/clintox.zip'107        data_path = get_download_dir() + '/clintox.zip'108        dir_path = get_download_dir() + '/clintox'109        download(_get_dgl_url(self._url), path=data_path, overwrite=False)110        extract_archive(data_path, dir_path)111        df = pd.read_csv(dir_path + '/clintox.csv')112        super(ClinTox, self).__init__(df=df,113                                      smiles_to_graph=smiles_to_graph,114                                      smiles_column='smiles',115                                      cache_file_path=cache_file_path,116                                      load=load,117                                      log_every=log_every,118                                      init_mask=True,119                                      n_jobs=n_jobs)120    def __getitem__(self, item):121        return self.smiles[item], self.graphs[item], self.labels[item], self.mask[item]122class SIDER(MoleculeCSVDataset):123    def __init__(self,124                 smiles_to_graph=smiles_2_dgl,125                 load=False,126                 log_every=1000,127                 cache_file_path='./sider_dglgraph.bin',128                 n_jobs=1):129        self._url = 'dataset/sider.zip'130        data_path = get_download_dir() + '/sider.zip'131        dir_path = get_download_dir() + '/sider'132        download(_get_dgl_url(self._url), path=data_path, overwrite=False)133        extract_archive(data_path, dir_path)134        df = pd.read_csv(dir_path + '/sider.csv')135        super(SIDER, self).__init__(df=df,136                                    smiles_to_graph=smiles_to_graph,137                                    smiles_column='smiles',138                                    cache_file_path=cache_file_path,139                                    load=load,140                                    log_every=log_every,141                                    init_mask=True,142                                    n_jobs=n_jobs)143    def __getitem__(self, item):144        return self.smiles[item], self.graphs[item], self.labels[item], self.mask[item]145class ToxCast(MoleculeCSVDataset):146    def __init__(self,147                 smiles_to_graph=smiles_2_dgl,148                 load=False,149                 log_every=1000,150                 cache_file_path='./toxcast_dglgraph.bin',151                 n_jobs=1):152        self._url = 'dataset/toxcast.zip'153        data_path = get_download_dir() + '/toxcast.zip'154        dir_path = get_download_dir() + '/toxcast'155        download(_get_dgl_url(self._url), path=data_path, overwrite=False)156        extract_archive(data_path, dir_path)157        df = pd.read_csv(dir_path + '/toxcast_data.csv')158        super(ToxCast, self).__init__(df=df,159                                      smiles_to_graph=smiles_to_graph,160                                      smiles_column='smiles',161                                      cache_file_path=cache_file_path,162                                      load=load,163                                      log_every=log_every,164                                      init_mask=True,165                                      n_jobs=n_jobs)166    def __getitem__(self, item):167        return self.smiles[item], self.graphs[item], self.labels[item], self.mask[item]168class HIV(MoleculeCSVDataset):169    def __init__(self,170                 smiles_to_graph=smiles_2_dgl,171                 load=False,172                 log_every=1000,173                 cache_file_path='./hiv_dglgraph.bin',174                 n_jobs=1):175        self._url = 'dataset/hiv.zip'176        data_path = get_download_dir() + '/hiv.zip'177        dir_path = get_download_dir() + '/hiv'178        download(_get_dgl_url(self._url), path=data_path, overwrite=False)179        extract_archive(data_path, dir_path)180        df = pd.read_csv(dir_path + '/HIV.csv')181        self.activity = df['activity'].tolist()182        self.load_full = False183        df = df.drop(columns=['activity'])184        super(HIV, self).__init__(df=df,185                                  smiles_to_graph=smiles_to_graph,186                                  smiles_column='smiles',187                                  cache_file_path=cache_file_path,188                                  load=load,189                                  log_every=log_every,190                                  init_mask=True,191                                  n_jobs=n_jobs)192        self.activity = [self.activity[i] for i in self.valid_ids]193    def __getitem__(self, item):194        if self.load_full:195            return self.smiles[item], self.graphs[item], self.labels[item], \196                   self.mask[item], self.activity[item]197        else:198            return self.smiles[item], self.graphs[item], self.labels[item], self.mask[item]199class Tox21(MoleculeCSVDataset):200    def __init__(self, smiles_to_graph=smiles_2_dgl,201                 load=False,202                 log_every=1000,203                 cache_file_path='./tox21_dglgraph.bin',204                 n_jobs=1):205        self._url = 'dataset/tox21.csv.gz'206        data_path = get_download_dir() + '/tox21.csv.gz'207        download(_get_dgl_url(self._url), path=data_path, overwrite=False)208        df = pd.read_csv(data_path)209        self.id = df['mol_id']210        df = df.drop(columns=['mol_id'])211        self.load_full = False212        super(Tox21, self).__init__(df, smiles_to_graph, 213                                    smiles_column='smiles',214                                    cache_file_path=cache_file_path,215                                    load=load, log_every=log_every, n_jobs=n_jobs)216        self.id = [self.id[i] for i in self.valid_ids]217    def __getitem__(self, item):218        if self.load_full:219            return self.smiles[item], self.graphs[item], self.labels[item], \220                   self.mask[item], self.id[item]221        else:222            return self.smiles[item], self.graphs[item], self.labels[item], self.mask[item]223class ESOL(MoleculeCSVDataset):224    def __init__(self,225                 smiles_to_graph=smiles_2_dgl,226                 load=False,227                 log_every=1000,228                 cache_file_path='./esol_dglgraph.bin',229                 n_jobs=1):230        self._url = 'dataset/ESOL.zip'231        data_path = get_download_dir() + '/ESOL.zip'232        dir_path = get_download_dir() + '/ESOL'233        download(_get_dgl_url(self._url), path=data_path, overwrite=False)234        extract_archive(data_path, dir_path)235        df = pd.read_csv(dir_path + '/delaney-processed.csv')236        super(ESOL, self).__init__(df=df,237                                   smiles_to_graph=smiles_to_graph,238                                   smiles_column='smiles',239                                   cache_file_path=cache_file_path,240                                   task_names=['measured log solubility in mols per litre'],241                                   load=load,242                                   log_every=log_every,243                                   init_mask=False,244                                   n_jobs=n_jobs)245        self.load_full = False246        # Compound names in PubChem247        self.compound_names = df['Compound ID'].tolist()248        self.compound_names = [self.compound_names[i] for i in self.valid_ids]249        # Estimated solubility250        self.estimated_solubility = df['ESOL predicted log solubility in mols per litre'].tolist()251        self.estimated_solubility = [self.estimated_solubility[i] for i in self.valid_ids]252        # Minimum atom degree253        self.min_degree = df['Minimum Degree'].tolist()254        self.min_degree = [self.min_degree[i] for i in self.valid_ids]255        # Molecular weight256        self.mol_weight = df['Molecular Weight'].tolist()257        self.mol_weight = [self.mol_weight[i] for i in self.valid_ids]258        # Number of H-Bond Donors259        self.num_h_bond_donors = df['Number of H-Bond Donors'].tolist()260        self.num_h_bond_donors = [self.num_h_bond_donors[i] for i in self.valid_ids]261        # Number of rings262        self.num_rings = df['Number of Rings'].tolist()263        self.num_rings = [self.num_rings[i] for i in self.valid_ids]264        # Number of rotatable bonds265        self.num_rotatable_bonds = df['Number of Rotatable Bonds'].tolist()266        self.num_rotatable_bonds = [self.num_rotatable_bonds[i] for i in self.valid_ids]267        # Polar Surface Area268        self.polar_surface_area = df['Polar Surface Area'].tolist()269        self.polar_surface_area = [self.polar_surface_area[i] for i in self.valid_ids]270    def __getitem__(self, item):271        if self.load_full:272            return self.smiles[item], self.graphs[item], self.labels[item], \273                   self.compound_names[item], self.estimated_solubility[item], \274                   self.min_degree[item], self.mol_weight[item], \275                   self.num_h_bond_donors[item], self.num_rings[item], \276                   self.num_rotatable_bonds[item], self.polar_surface_area[item]277        else:278            return self.smiles[item], self.graphs[item], self.labels[item]279class FreeSolv(MoleculeCSVDataset):280    def __init__(self,281                 smiles_to_graph=smiles_2_dgl,282                 load=False,283                 log_every=1000,284                 cache_file_path='./freesolv_dglgraph.bin',285                 n_jobs=1):286        self._url = 'dataset/FreeSolv.zip'287        data_path = get_download_dir() + '/FreeSolv.zip'288        dir_path = get_download_dir() + '/FreeSolv'289        download(_get_dgl_url(self._url), path=data_path, overwrite=False)290        extract_archive(data_path, dir_path)291        df = pd.read_csv(dir_path + '/SAMPL.csv')292        super(FreeSolv, self).__init__(df=df,293                                       smiles_to_graph=smiles_to_graph,294                                       smiles_column='smiles',295                                       cache_file_path=cache_file_path,296                                       task_names=['expt'],297                                       load=load,298                                       log_every=log_every,299                                       init_mask=False,300                                       n_jobs=n_jobs)301        self.load_full = False302        # Iupac names303        self.iupac_names = df['iupac'].tolist()304        self.iupac_names = [self.iupac_names[i] for i in self.valid_ids]305        # Calculated hydration free energy306        self.calc_energy = df['calc'].tolist()307        self.calc_energy = [self.calc_energy[i] for i in self.valid_ids]308    def __getitem__(self, item):309        if self.load_full:310            return self.smiles[item], self.graphs[item], self.labels[item], \311                   self.iupac_names[item], self.calc_energy[item]312        else:313            return self.smiles[item], self.graphs[item], self.labels[item]314class Lipophilicity(MoleculeCSVDataset):315    def __init__(self,316                 smiles_to_graph=smiles_2_dgl,317                 load=False,318                 log_every=1000,319                 cache_file_path='./lipophilicity_dglgraph.bin',320                 n_jobs=1):321        self._url = 'dataset/lipophilicity.zip'322        data_path = get_download_dir() + '/lipophilicity.zip'323        dir_path = get_download_dir() + '/lipophilicity'324        download(_get_dgl_url(self._url), path=data_path, overwrite=False)325        extract_archive(data_path, dir_path)326        df = pd.read_csv(dir_path + '/Lipophilicity.csv')327        super(Lipophilicity, self).__init__(df=df,328                                            smiles_to_graph=smiles_to_graph,329                                            smiles_column='smiles',330                                            cache_file_path=cache_file_path,331                                            task_names=['exp'],332                                            load=load,333                                            log_every=log_every,334                                            init_mask=False,335                                            n_jobs=n_jobs)336        self.load_full = False337        # ChEMBL ids...

mmap_libt0.4_pp0_fp2.0_3.py

Source:mmap_libt0.4_pp0_fp2.0_3.py

...10import zipfile11from six.moves import urllib12from . import config13logger = logging.getLogger(__name__)14def get_download_dir():15    """Return the directory in which downloaded and converted datasets are stored.16    This directory is defined by the environment variable ``CHAINER_DATASET_ROOT``.17    If the environment variable is not specified, it defaults to ``$HOME/.chainer/dataset``.18    Returns:19        str: The path to the download directory.20    """21    return config.get_download_dir()22def get_dataset_directory(dataset_name):23    """Return the directory in which the given dataset is stored.24    Args:25        dataset_name (str): The name of the dataset.26    Returns:27        str: The path to the dataset directory.28    """...

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