def make_dataset(name,
data,
table_name=None,
entity_columns=None,
sequence_index=None,
datasets_path='.'):
"""Make a Dataset from a DataFrame.
Args:
name (str):
Name of this dataset.
data (pandas.DataFrame or str):
Data passed as a DataFrame or as a path to a CSV file.
table_name (str or None):
Optionally give the table a different name.
entity_columns (list or None):
(Optional) List of names of the columns that form the entity_id of this
dataset. If ``None`` (default), no entity columns are set.
sequence_index (str or None):
(Optional) Name of the column that is the sequence index of this dataset.
datasets_path (str):
(Optional) Path to the folder in which a new folder will be created
for this dataset. Defaults to the current working directory.
"""
if isinstance(data, str):
data = pd.read_csv(data)
base_path = os.path.join(datasets_path, name)
if os.path.exists(base_path):
shutil.rmtree(base_path)
os.makedirs(base_path, exist_ok=True)
table_name = table_name or name
cwd = os.getcwd()
try:
os.chdir(base_path)
csv_name = table_name + '.csv'
data.to_csv(csv_name, index=False)
metadata = Metadata()
metadata.add_table(name, csv_name)
meta_dict = metadata.to_dict()
table_meta = meta_dict['tables'][table_name]
table_meta['entity_columns'] = entity_columns or []
table_meta['sequence_index'] = sequence_index
table_meta['deepecho_version'] = Dataset.VERSION
with open('metadata.json', 'w') as metadata_file:
json.dump(meta_dict, metadata_file, indent=4)
LOGGER.info('Dataset %s generated in folder %s', name, base_path)
finally:
os.chdir(cwd)
def load_multi_foreign_key():
parent = pd.DataFrame({
'parent_id': range(10),
'value': range(10)
})
child = pd.DataFrame({
'parent_1_id': range(10),
'parent_2_id': range(10),
'value': range(10)
})
metadata = Metadata()
metadata.add_table('parent', parent, primary_key='parent_id')
metadata.add_table('child', child, parent='parent', foreign_key='parent_1_id')
metadata.add_relationship('parent', 'child', 'parent_2_id')
return metadata, {'parent': parent, 'child': child}
def test_evaluate_tables_from_demo():
tables = load_demo(metadata=False)
new_meta = Metadata()
new_meta.add_table('users', data=tables['users'], primary_key='user_id')
new_meta.add_table('sessions',
data=tables['sessions'],
primary_key='session_id',
parent='users',
foreign_key='user_id')
transactions_fields = {
'timestamp': {
'type': 'datetime',
'format': '%Y-%m-%d'
}
}
new_meta.add_table('transactions',
tables['transactions'],
fields_metadata=transactions_fields,
primary_key='transaction_id',
parent='sessions')
sdv = SDV()
sdv.fit(new_meta, tables=tables)
sampled = sdv.sample_all()
table_scores = dict()
for table in new_meta.get_tables():
table_scores[table] = evaluate(sampled[table],
real=tables[table],
metadata=new_meta,
table_name=table)
evaluate(sampled, real=tables, metadata=new_meta)
def test_build_demo_metadata_from_tables():
"""Build metadata from the demo tables.
Then compare the built metadata with the demo one
to make sure that they are the same.
"""
tables = load_demo(metadata=False)
new_meta = Metadata()
new_meta.add_table('users', data=tables['users'], primary_key='user_id')
new_meta.add_table('sessions',
data=tables['sessions'],
primary_key='session_id',
parent='users',
foreign_key='user_id')
transactions_fields = {
'timestamp': {
'type': 'datetime',
'format': '%Y-%m-%dT%H:%M'
}
}
new_meta.add_table('transactions',
tables['transactions'],
fields_metadata=transactions_fields,
primary_key='transaction_id',
parent='sessions')
assert DEMO_METADATA == new_meta.to_dict()
def test_build_demo_metadata_without_tables():
metadata = Metadata()
metadata.add_table('users')
metadata.add_field('users', 'user_id', 'id', 'integer')
metadata.add_field('users', 'country', 'categorical')
metadata.add_field('users', 'gender', 'categorical')
metadata.add_field('users', 'age', 'numerical', 'integer')
metadata.set_primary_key('users', 'user_id')
metadata.add_table('sessions')
metadata.add_field('sessions', 'session_id', 'id', 'integer')
metadata.add_field('sessions', 'user_id', 'id', 'integer')
metadata.add_field('sessions', 'device', 'categorical')
metadata.add_field('sessions', 'os', 'categorical')
metadata.add_field('sessions', 'minutes', 'numerical', 'integer')
metadata.set_primary_key('sessions', 'session_id')
metadata.add_relationship('users', 'sessions')
metadata.add_table('transactions')
metadata.add_field('transactions', 'transaction_id', 'id', 'integer')
metadata.add_field('transactions', 'session_id', 'id', 'integer')
metadata.add_field('transactions',
'timestamp',
'datetime',
properties={'format': '%Y-%m-%dT%H:%M'})
metadata.add_field('transactions', 'amount', 'numerical', 'float')
metadata.add_field('transactions', 'cancelled', 'boolean')
metadata.set_primary_key('transactions', 'transaction_id')
metadata.add_relationship('sessions', 'transactions')
assert DEMO_METADATA == metadata.to_dict()
pd.DataFrame({
"x": np.random.random(size=size),
"y": np.random.normal(size=size, loc=10.0)
})
}
lq_synthetic = {
"table1":
pd.DataFrame({
"x":
np.random.random(size=size) + np.random.normal(size=size),
"y":
np.random.normal(size=size, loc=10.0) + np.random.normal(size=size)
})
}
hq_synthetic = {
"table1":
pd.DataFrame({
"x":
np.random.random(size=size) + np.random.normal(size=size) / 10.0,
"y":
np.random.normal(size=size, loc=10.0) +
np.random.normal(size=size) / 10.0
})
}
metadata = Metadata()
for table_name, df in tables.items():
metadata.add_table(table_name, data=df)
dataset = Dataset(metadata, tables, lq_synthetic, hq_synthetic)
dataset.save(os.path.dirname(__file__))
fake.address().replace('\n', '') for _ in range(suppliers.shape[0])
]
suppliers.to_csv('Suppliers.csv', index=False)
tables = {
'Products': products,
'Suppliers': suppliers,
'Customers': customers,
'Sales orders': sales_orders,
'Purchase orders': purchase_orders
}
metadata = Metadata()
metadata.add_table(name='Products',
data=tables['Products'],
primary_key='Product id')
metadata.add_table(name='Sales orders',
data=tables['Sales orders'],
primary_key='Sales order id',
foreign_key='Product id',
parent='Products')
metadata.add_table(name='Purchase orders',
data=tables['Purchase orders'],
primary_key='Purchase order id',
foreign_key='Product id',
parent='Products')
metadata.add_table(name='Customers',
data=tables['Customers'],
primary_key='Customer id')
metadata.add_relationship(parent='Customers',
dataset_name = dataset_name.replace(".ts", "")
dataset_name = dataset_name.replace("_TRAIN", "")
dataset_dir = "datasets/%s" % dataset_name
os.makedirs(dataset_dir, exist_ok=True)
path_to_test = path_to_train.replace("_TRAIN", "_TEST")
path_to_csv = os.path.join(dataset_dir, "%s.csv" % dataset_name)
path_to_metadata = os.path.join(dataset_dir, "metadata.json")
path_to_readme = os.path.join(dataset_dir, "README.md")
print(path_to_csv, path_to_metadata, path_to_readme)
df = to_our_format(path_to_train, path_to_test)
df.to_csv(path_to_csv, index=False)
metadata = Metadata()
metadata.add_table('data', data=df, primary_key='e_id')
metadata.to_json(path_to_metadata)
with open(os.path.join(dataset_dir, "task.json"), "wt") as fout:
json.dump({
"task_type": "classification",
"key": ["e_id"],
"target": "ml_class",
"ignored": ["tt_split", "s_index"]
}, fout)
with open(path_to_readme, "wt") as fout:
fout.write("""# %s
This dataset originates from the Time Series Classification
dataset repository (http://www.timeseriesclassification.com/).
