def write_output_dataset(self, labels, images, output_dir):
"""Create output directories where we write out the images.
:args:
labels (str) : the labels for the image
data (np.array) : the binary array corresponding to the image
output_dir (str) : the output directory that we need to write to
path (str): the raw dataset path
:returns:
A tuple of the label for the image, the file array, the size and rows and columns for the image
"""
# create child image output directories
output_dirs = [
os.path.join(output_dir, str(i)) for i in range(NUM_LABELS)
]
for output_dir in output_dirs:
makedirs(output_dir, exist_ok=True)
def write_processed_image(t):
i, label = t
output_filename = os.path.join(output_dirs[label], str(i) + ".png")
torch_image = torch.from_numpy(images[i].copy()).view(1, 28, 28)
self.write_png(torch_image, output_filename)
# write out image data
tasks = list(enumerate(labels))
pool = ThreadPool(NUM_LABELS)
pool.map(write_processed_image, tasks)
pool.close()
pool.join()
def process_downloaded_dataset(self):
df = pd.read_csv(os.path.join(self.raw_dataset_path, "UCI CBM Dataset",
"data.txt"),
header=None,
sep=" ")
columns = [
"lp",
"v",
"gtt",
"gtn",
"ggn",
"ts",
"tp",
"t48",
"t1",
"t2",
"p48",
"p1",
"p2",
"pexh",
"tic",
"mf",
"gtcdsc",
"gttdsc",
]
df.columns = columns
makedirs(self.processed_temp_path, exist_ok=True)
df.to_csv(os.path.join(self.processed_temp_path, self.csv_filename),
index=False)
rename(self.processed_temp_path, self.processed_dataset_path)
def process_downloaded_dataset(self):
downloaded_files = self.download_filenames
filetype = self.download_file_type
train_files = ["train_identity.csv", "train_transaction.csv"]
test_files = ["test_identity.csv", "test_transaction.csv"]
train_dfs, test_dfs = {}, {}
for split_name, filename in downloaded_files.items():
file_df = self.read_file(filetype, filename, header=0)
if filename in train_files:
train_dfs[split_name] = file_df
elif filename in test_files:
test_dfs[split_name] = file_df
# Merge on TransactionID
final_train = pd.merge(train_dfs["train_transaction"],
train_dfs["train_identity"],
on="TransactionID",
how="left")
makedirs(self.processed_dataset_path)
# Only save train split as test split has no ground truth labels
final_train.to_csv(os.path.join(self.processed_dataset_path,
self.csv_filename),
index=False)
def dask_to_tfrecords(df,
folder,
compression_type="GZIP",
compression_level=9):
"""Store Dask.dataframe to TFRecord files."""
makedirs(folder, exist_ok=True)
compression_ext = get_compression_ext(compression_type)
filenames = [
get_part_filename(i, compression_ext) for i in range(df.npartitions)
]
# Also write a meta data file
write_meta(df, folder, compression_type)
dsk = {}
name = "to-tfrecord-" + tokenize(df, folder)
part_tasks = []
kwargs = {}
for d, filename in enumerate(filenames):
dsk[(name, d)] = (apply, pandas_df_to_tfrecords, [
(df._name, d),
os.path.join(folder, filename), compression_type, compression_level
], kwargs)
part_tasks.append((name, d))
dsk[name] = (lambda x: None, part_tasks)
graph = HighLevelGraph.from_collections(name, dsk, dependencies=[df])
out = Delayed(name, graph)
out = out.compute()
return out
def process_downloaded_dataset(self):
df = pd.read_csv(
os.path.join(self.raw_dataset_path, 'HIGGS.csv.gz'),
header=None
)
df.columns = [
"label", "lepton_pT", "lepton_eta", "lepton_phi",
"missing_energy_magnitude", "missing_energy_phi",
"jet_1_pt", "jet_1_eta", "jet_1_phi", "jet_1_b-tag",
"jet_2_pt", "jet_2_eta", "jet_2_phi", "jet_2_b-tag",
"jet_3_pt", "jet_3_eta", "jet_3_phi", "jet_3_b-tag",
"jet_4_pt", "jet_4_eta", "jet_4_phi", "jet_4_b-tag",
"m_jj", "m_jjj", "m_lv", "m_jlv", "m_bb", "m_wbb", "m_wwbb"
]
df['label'] = df['label'].astype('int32')
if self.add_validation_set:
df['split'] = [0] * 10000000 + [1] * 500000 + [2] * 500000
else:
df['split'] = [0] * 10500000 + [2] * 500000
makedirs(self.processed_temp_path, exist_ok=True)
df.to_parquet(os.path.join(self.processed_temp_path, self.parquet_filename),
engine='pyarrow',
row_group_size=50000,
index=False)
rename(self.processed_temp_path, self.processed_dataset_path)
def process_downloaded_dataset(self, header=0):
"""Processes dataset
:param header: indicates whether raw data files contain headers
"""
downloaded_files = self.download_filenames
filetype = self.download_file_type
all_files = []
for split_name, filename in downloaded_files.items():
file_df = self.read_file(filetype, filename, header)
if split_name == 'train_file':
file_df['split'] = 0
elif split_name == 'val_file':
file_df['split'] = 1
elif split_name == 'test_file':
file_df['split'] = 2
else:
raise ValueError(f'Unrecognized split name: {split_name}')
all_files.append(file_df)
concat_df = pd.concat(all_files, ignore_index=True)
makedirs(self.processed_dataset_path, exist_ok=True)
concat_df.to_csv(os.path.join(self.processed_dataset_path,
self.csv_filename),
index=False)
def process_downloaded_dataset(self):
train_df = pd.read_csv(os.path.join(self.raw_dataset_path,
"adult.data"),
header=None)
test_df = pd.read_csv(os.path.join(self.raw_dataset_path,
"adult.test"),
header=None,
skiprows=1)
# age: continuous.
# workclass: Private, Self-emp-not-inc, Self-emp-inc, Federal-gov, Local-gov, State-gov, Without-pay, Never-worked. # noqa: E501
# fnlwgt: continuous.
# education: Bachelors, Some-college, 11th, HS-grad, Prof-school, Assoc-acdm, Assoc-voc, 9th, 7th-8th, 12th, Masters, 1st-4th, 10th, Doctorate, 5th-6th, Preschool. # noqa: E501
# education-num: continuous.
# marital-status: Married-civ-spouse, Divorced, Never-married, Separated, Widowed, Married-spouse-absent, Married-AF-spouse. # noqa: E501
# occupation: Tech-support, Craft-repair, Other-service, Sales, Exec-managerial, Prof-specialty, Handlers-cleaners, Machine-op-inspct, Adm-clerical, Farming-fishing, Transport-moving, Priv-house-serv, Protective-serv, Armed-Forces. # noqa: E501
# relationship: Wife, Own-child, Husband, Not-in-family, Other-relative, Unmarried.
# race: White, Asian-Pac-Islander, Amer-Indian-Eskimo, Other, Black.
# sex: Female, Male.
# capital-gain: continuous.
# capital-loss: continuous.
# hours-per-week: continuous.
# native-country: United-States, Cambodia, England, Puerto-Rico, Canada, Germany, Outlying-US(Guam-USVI-etc), India, Japan, Greece, South, China, Cuba, Iran, Honduras, Philippines, Italy, Poland, Jamaica, Vietnam, Mexico, Portugal, Ireland, France, Dominican-Republic, Laos, Ecuador, Taiwan, Haiti, Columbia, Hungary, Guatemala, Nicaragua, Scotland, Thailand, Yugoslavia, El-Salvador, Trinadad&Tobago, Peru, Hong, Holand-Netherlands. # noqa: E501
# income: >50K, <=50K.
columns = [
"age",
"workclass",
"fnlwgt",
"education",
"education-num",
"marital-status",
"occupation",
"relationship",
"race",
"sex",
"capital-gain",
"capital-loss",
"hours-per-week",
"native-country",
"income",
]
train_df.columns = columns
test_df.columns = columns
# Remove the trailing period on the income field in adult.test (not in adult.data)
test_df["income"] = test_df["income"].str.rstrip(".")
train_df["split"] = 0
test_df["split"] = 2
df = pd.concat([train_df, test_df])
# remove trailing space
df["income"] = df["income"].str.strip()
makedirs(self.processed_temp_path, exist_ok=True)
df.to_csv(os.path.join(self.processed_temp_path, self.csv_filename),
index=False)
rename(self.processed_temp_path, self.processed_dataset_path)
def process_downloaded_dataset(self):
"""The final method where we create a concatenated CSV file with both training ant dest data."""
train_file = self.config["split_filenames"]["train_file"]
df = pd.read_csv(os.path.join(self.raw_dataset_path, train_file))
df[SPLIT] = df.index.to_series().map(lambda x: np.random.choice(3, 1, p=(0.7, 0.1, 0.2))).astype(np.int8)
makedirs(self.processed_temp_path, exist_ok=True)
df.to_csv(os.path.join(self.processed_temp_path, self.csv_filename), index=False)
rename(self.processed_temp_path, self.processed_dataset_path)
def to_tfrecord(self, df, path):
compression_type = "GZIP"
compression_ext = get_compression_ext(compression_type)
makedirs(path, exist_ok=True)
write_meta(df, path, compression_type)
filename = os.path.join(path, get_part_filename(0, compression_ext))
pandas_df_to_tfrecords(df,
filename,
compression_type=compression_type,
compression_level=9)
def process_downloaded_dataset(self):
"""Read the training and test directories and write out a csv containing the training path and the
label."""
makedirs(self.processed_temp_path, exist_ok=True)
for dataset in ["training", "testing"]:
print(f">>> create ludwig formatted {dataset} data")
labels, data = self.read_source_dataset(dataset,
self.raw_dataset_path)
self.write_output_dataset(
labels, data, os.path.join(self.processed_temp_path, dataset))
self.output_training_and_test_data()
rename(self.processed_temp_path, self.processed_dataset_path)
print(">>> completed data preparation")
def process_downloaded_dataset(self, header=0):
zip_file = ZipFile(
os.path.join(self.raw_dataset_path, "orange_small_train.data.zip"))
train_df = pd.read_csv(zip_file.open("orange_small_train.data"),
sep="\t")
zip_file = ZipFile(
os.path.join(self.raw_dataset_path, "orange_small_test.data.zip"))
test_df = pd.read_csv(zip_file.open("orange_small_test.data"),
sep="\t")
train_df = process_categorical_features(train_df, categorical_features)
train_df = process_numerical_features(train_df, categorical_features)
targets = (pd.read_csv(os.path.join(
self.raw_dataset_path,
f"orange_small_train_{self.task_name}.labels"),
header=None)[0].astype(str).apply(
lambda x: "true" if x == "1" else "false"))
train_idcs = pd.read_csv(os.path.join(
self.raw_dataset_path,
f"stratified_train_idx_{self.task_name}.txt"),
header=None)[0]
val_idcs = pd.read_csv(os.path.join(
self.raw_dataset_path,
f"stratified_test_idx_{self.task_name}.txt"),
header=None)[0]
processed_train_df = train_df.iloc[train_idcs].copy()
processed_train_df["target"] = targets.iloc[train_idcs]
processed_train_df["split"] = 0
processed_val_df = train_df.iloc[val_idcs].copy()
processed_val_df["target"] = targets.iloc[val_idcs]
processed_val_df["split"] = 1
test_df["target"] = ""
test_df["split"] = 2
df = pd.concat([processed_train_df, processed_val_df, test_df])
makedirs(self.processed_temp_path, exist_ok=True)
df.to_csv(os.path.join(self.processed_temp_path, self.csv_filename),
index=False)
rename(self.processed_temp_path, self.processed_dataset_path)
def process_downloaded_dataset(self):
raw_df = pd.read_csv(
os.path.join(self.raw_dataset_path, self.data_filename))
columns = [
"sepal_length_cm",
"sepal_width_cm",
"petal_length_cm",
"petal_width_cm",
"class",
]
raw_df.columns = columns
makedirs(self.processed_dataset_path, exist_ok=True)
raw_df.to_csv(os.path.join(self.processed_dataset_path,
self.csv_filename),
index=False)
def process_downloaded_dataset(self):
stores_df = pd.read_csv(os.path.join(self.raw_dataset_path, "store.csv"))
train_df = pd.read_csv(os.path.join(self.raw_dataset_path, "train.csv"), low_memory=False)
train_df = preprocess_df(train_df, stores_df)
train_df["split"] = -1
train_df.loc[train_df["Year"] == 2014, "split"] = 0
train_df.loc[train_df["Year"] == 2015, "split"] = 2
train_df.drop(train_df[train_df["split"] == -1].index, inplace=True)
df = train_df
makedirs(self.processed_temp_path, exist_ok=True)
df.to_csv(os.path.join(self.processed_temp_path, self.csv_filename), index=False)
rename(self.processed_temp_path, self.processed_dataset_path)
def process_downloaded_dataset(self):
"""The final method where we create a concatenated CSV file with both training ant dest data."""
train_file = self.config["split_filenames"]["train_file"]
test_file = self.config["split_filenames"]["test_file"]
train_df = pd.read_csv(os.path.join(self.raw_dataset_path, train_file))
test_df = pd.read_csv(os.path.join(self.raw_dataset_path, test_file))
train_df["split"] = 0
test_df["split"] = 2
df = pd.concat([train_df, test_df])
makedirs(self.processed_temp_path, exist_ok=True)
df.to_csv(os.path.join(self.processed_temp_path, self.csv_filename),
index=False)
rename(self.processed_temp_path, self.processed_dataset_path)
def process_downloaded_dataset(self):
makedirs(self.processed_temp_path, exist_ok=True)
# create a dictionary matching image_path --> list of captions
image_to_caption = defaultdict(list)
with open(
f"{self.raw_dataset_path}/Flickr8k.token.txt",
"r"
) as captions_file:
image_to_caption = defaultdict(list)
for line in captions_file:
line = line.split("#")
# the regex is to format the string to fit properly in a csv
line[1] = line[1].strip("\n01234.\t ")
line[1] = re.sub('\"', '\"\"', line[1])
line[1] = '\"' + line[1] + '\"'
image_to_caption[line[0]].append(line[1])
# create csv file with 7 columns: image_path, 5 captions, and split
with open(
os.path.join(self.processed_temp_path, self.csv_filename),
'w'
) as output_file:
output_file.write('image_path,caption0,caption1,caption2,')
output_file.write('caption3,caption4,split\n')
splits = ["train", "dev", "test"]
for i in range(len(splits)):
split = splits[i]
with open(
f"{self.raw_dataset_path}/Flickr_8k.{split}Images.txt",
"r"
) as split_file:
for image_name in split_file:
image_name = image_name.strip('\n')
if image_name in image_to_caption:
output_file.write('{},{},{},{},{},{},{}\n'.format(
# Note: image folder is named Flicker8k_Dataset
"{}/Flicker8k_Dataset/{}".format(
self.raw_dataset_path, image_name
),
*image_to_caption[image_name],
i
))
# Note: csv is stored in /processed while images are stored in /raw
rename(self.processed_temp_path, self.processed_dataset_path)
def process_downloaded_dataset(self):
makedirs(self.processed_temp_path, exist_ok=True)
dataset_name = self.config["kaggle_dataset_name"]
for url in self.config["kaggle_dataset_files"]:
file_name = os.path.join(self.raw_dataset_path, dataset_name, url)
# TODO(shreya): DataFrame created twice: here + CSVMixin. Figure out
# options for using it once.
df = pd.read_csv(
file_name,
header=0,
names=[
"image_path",
"insurance_company",
"cost_of_vehicle",
"min_coverage",
"expiry_date",
"max_coverage",
"condition",
"amount",
],
)
df["image_path"] = df["image_path"].apply(
lambda x: os.path.join(self.raw_dataset_path, dataset_name, "trainImages", os.path.basename(x))
)
df.to_csv(
os.path.join(self.processed_temp_path, self.csv_filename),
columns=[
"image_path",
"insurance_company",
"cost_of_vehicle",
"min_coverage",
"expiry_date",
"max_coverage",
"condition",
"amount",
],
)
# Note: csv is stored in /processed while images are stored in /raw
rename(self.processed_temp_path, self.processed_dataset_path)
def process_downloaded_dataset(self):
df = pd.read_csv(os.path.join(self.raw_dataset_path, "covtype.data.gz"), header=None)
# Elevation quantitative meters Elevation in meters
# Aspect quantitative azimuth Aspect in degrees azimuth
# Slope quantitative degrees Slope in degrees
# Horizontal_Distance_To_Hydrology quantitative meters Horz Dist to nearest surface water features # noqa: E501
# Vertical_Distance_To_Hydrology quantitative meters Vert Dist to nearest surface water features # noqa: E501
# Horizontal_Distance_To_Roadways quantitative meters Horz Dist to nearest roadway # noqa: E501
# Hillshade_9am quantitative 0 to 255 index Hillshade index at 9am, summer solstice # noqa: E501
# Hillshade_Noon quantitative 0 to 255 index Hillshade index at noon, summer soltice # noqa: E501
# Hillshade_3pm quantitative 0 to 255 index Hillshade index at 3pm, summer solstice # noqa: E501
# Horizontal_Distance_To_Fire_Points quantitative meters Horz Dist to nearest wildfire ignition points # noqa: E501
# Wilderness_Area (4 binary columns) qualitative 0 (absence) or 1 (presence) Wilderness area designation # noqa: E501
# Soil_Type (40 binary columns) qualitative 0 (absence) or 1 (presence) Soil Type designation
# Cover_Type (7 types) integer 1 to 7 Forest Cover Type designation # noqa: E501
columns = [
"Elevation",
"Aspect",
"Slope",
"Horizontal_Distance_To_Hydrology",
"Vertical_Distance_To_Hydrology",
"Horizontal_Distance_To_Roadways",
"Hillshade_9am",
"Hillshade_Noon",
"Hillshade_3pm",
"Horizontal_Distance_To_Fire_Points",
"Wilderness_Area_1",
"Wilderness_Area_2",
"Wilderness_Area_3",
"Wilderness_Area_4",
"Soil_Type_1",
"Soil_Type_2",
"Soil_Type_3",
"Soil_Type_4",
"Soil_Type_5",
"Soil_Type_6",
"Soil_Type_7",
"Soil_Type_8",
"Soil_Type_9",
"Soil_Type_10",
"Soil_Type_11",
"Soil_Type_12",
"Soil_Type_13",
"Soil_Type_14",
"Soil_Type_15",
"Soil_Type_16",
"Soil_Type_17",
"Soil_Type_18",
"Soil_Type_19",
"Soil_Type_20",
"Soil_Type_21",
"Soil_Type_22",
"Soil_Type_23",
"Soil_Type_24",
"Soil_Type_25",
"Soil_Type_26",
"Soil_Type_27",
"Soil_Type_28",
"Soil_Type_29",
"Soil_Type_30",
"Soil_Type_31",
"Soil_Type_32",
"Soil_Type_33",
"Soil_Type_34",
"Soil_Type_35",
"Soil_Type_36",
"Soil_Type_37",
"Soil_Type_38",
"Soil_Type_39",
"Soil_Type_40",
"Cover_Type",
]
df.columns = columns
# Map the 40 soil types to a single integer
# instead of 40 binary columns
st_cols = [
"Soil_Type_1",
"Soil_Type_2",
"Soil_Type_3",
"Soil_Type_4",
"Soil_Type_5",
"Soil_Type_6",
"Soil_Type_7",
"Soil_Type_8",
"Soil_Type_9",
"Soil_Type_10",
"Soil_Type_11",
"Soil_Type_12",
"Soil_Type_13",
"Soil_Type_14",
"Soil_Type_15",
"Soil_Type_16",
"Soil_Type_17",
"Soil_Type_18",
"Soil_Type_19",
"Soil_Type_20",
"Soil_Type_21",
"Soil_Type_22",
"Soil_Type_23",
"Soil_Type_24",
"Soil_Type_25",
"Soil_Type_26",
"Soil_Type_27",
"Soil_Type_28",
"Soil_Type_29",
"Soil_Type_30",
"Soil_Type_31",
"Soil_Type_32",
"Soil_Type_33",
"Soil_Type_34",
"Soil_Type_35",
"Soil_Type_36",
"Soil_Type_37",
"Soil_Type_38",
"Soil_Type_39",
"Soil_Type_40",
]
st_vals = []
for _, row in df[st_cols].iterrows():
st_vals.append(row.to_numpy().nonzero()[0].item(0))
df = df.drop(columns=st_cols)
df["Soil_Type"] = st_vals
# Map the 4 wilderness areas to a single integer
# instead of 4 binary columns
wa_cols = ["Wilderness_Area_1", "Wilderness_Area_2", "Wilderness_Area_3", "Wilderness_Area_4"]
wa_vals = []
for _, row in df[wa_cols].iterrows():
wa_vals.append(row.to_numpy().nonzero()[0].item(0))
df = df.drop(columns=wa_cols)
df["Wilderness_Area"] = wa_vals
if not self.use_tabnet_split:
# first 11340 records used for training data subset
# next 3780 records used for validation data subset
# last 565892 records used for testing data subset
df["split"] = [0] * 11340 + [1] * 3780 + [2] * 565892
else:
# Split used in the tabNet paper
# https://github.com/google-research/google-research/blob/master/tabnet/download_prepare_covertype.py
train_val_indices, test_indices = train_test_split(range(len(df)), test_size=0.2, random_state=0)
train_indices, val_indices = train_test_split(train_val_indices, test_size=0.2 / 0.6, random_state=0)
df["split"] = 0
df.loc[val_indices, "split"] = 1
df.loc[test_indices, "split"] = 2
makedirs(self.processed_temp_path, exist_ok=True)
df.to_csv(os.path.join(self.processed_temp_path, self.csv_filename), index=False)
rename(self.processed_temp_path, self.processed_dataset_path)
def hyperopt(
config: Union[str, dict],
dataset: Union[str, dict, pd.DataFrame] = None,
training_set: Union[str, dict, pd.DataFrame] = None,
validation_set: Union[str, dict, pd.DataFrame] = None,
test_set: Union[str, dict, pd.DataFrame] = None,
training_set_metadata: Union[str, dict] = None,
data_format: str = None,
experiment_name: str = 'hyperopt',
model_name: str = 'run',
skip_save_training_description: bool = False,
skip_save_training_statistics: bool = False,
skip_save_model: bool = False,
skip_save_progress: bool = False,
skip_save_log: bool = False,
skip_save_processed_input: bool = True,
skip_save_unprocessed_output: bool = False,
skip_save_predictions: bool = False,
skip_save_eval_stats: bool = False,
skip_save_hyperopt_statistics: bool = False,
output_directory: str = 'results',
gpus: Union[str, int, List[int]] = None,
gpu_memory_limit: int = None,
allow_parallel_threads: bool = True,
backend: Union[Backend, str] = None,
random_seed: int = default_random_seed,
debug: bool = False,
**kwargs,
) -> HyperoptResults:
"""This method performs an hyperparameter optimization.
# Inputs
:param config: (Union[str, dict]) config which defines
the different parameters of the model, features, preprocessing and
training. If `str`, filepath to yaml configuration file.
:param dataset: (Union[str, dict, pandas.DataFrame], default: `None`)
source containing the entire dataset to be used in the experiment.
If it has a split column, it will be used for splitting (0 for train,
1 for validation, 2 for test), otherwise the dataset will be
randomly split.
:param training_set: (Union[str, dict, pandas.DataFrame], default: `None`)
source containing training data.
:param validation_set: (Union[str, dict, pandas.DataFrame], default: `None`)
source containing validation data.
:param test_set: (Union[str, dict, pandas.DataFrame], default: `None`)
source containing test data.
:param training_set_metadata: (Union[str, dict], default: `None`)
metadata JSON file or loaded metadata. Intermediate preprocessed
structure containing the mappings of the input
dataset created the first time an input file is used in the same
directory with the same name and a '.meta.json' extension.
:param data_format: (str, default: `None`) format to interpret data
sources. Will be inferred automatically if not specified. Valid
formats are `'auto'`, `'csv'`, `'df'`, `'dict'`, `'excel'`, `'feather'`,
`'fwf'`, `'hdf5'` (cache file produced during previous training),
`'html'` (file containing a single HTML `<table>`), `'json'`, `'jsonl'`,
`'parquet'`, `'pickle'` (pickled Pandas DataFrame), `'sas'`, `'spss'`,
`'stata'`, `'tsv'`.
:param experiment_name: (str, default: `'experiment'`) name for
the experiment.
:param model_name: (str, default: `'run'`) name of the model that is
being used.
:param skip_save_training_description: (bool, default: `False`) disables
saving the description JSON file.
:param skip_save_training_statistics: (bool, default: `False`) disables
saving training statistics JSON file.
:param skip_save_model: (bool, default: `False`) disables
saving model weights and hyperparameters each time the model
improves. By default Ludwig saves model weights after each epoch
the validation metric improves, but if the model is really big
that can be time consuming. If you do not want to keep
the weights and just find out what performance a model can get
with a set of hyperparameters, use this parameter to skip it,
but the model will not be loadable later on and the returned model
will have the weights obtained at the end of training, instead of
the weights of the epoch with the best validation performance.
:param skip_save_progress: (bool, default: `False`) disables saving
progress each epoch. By default Ludwig saves weights and stats
after each epoch for enabling resuming of training, but if
the model is really big that can be time consuming and will uses
twice as much space, use this parameter to skip it, but training
cannot be resumed later on.
:param skip_save_log: (bool, default: `False`) disables saving
TensorBoard logs. By default Ludwig saves logs for the TensorBoard,
but if it is not needed turning it off can slightly increase the
overall speed.
:param skip_save_processed_input: (bool, default: `False`) if input
dataset is provided it is preprocessed and cached by saving an HDF5
and JSON files to avoid running the preprocessing again. If this
parameter is `False`, the HDF5 and JSON file are not saved.
:param skip_save_unprocessed_output: (bool, default: `False`) by default
predictions and their probabilities are saved in both raw
unprocessed numpy files containing tensors and as postprocessed
CSV files (one for each output feature). If this parameter is True,
only the CSV ones are saved and the numpy ones are skipped.
:param skip_save_predictions: (bool, default: `False`) skips saving test
predictions CSV files.
:param skip_save_eval_stats: (bool, default: `False`) skips saving test
statistics JSON file.
:param skip_save_hyperopt_statistics: (bool, default: `False`) skips saving
hyperopt stats file.
:param output_directory: (str, default: `'results'`) the directory that
will contain the training statistics, TensorBoard logs, the saved
model and the training progress files.
:param gpus: (list, default: `None`) list of GPUs that are available
for training.
:param gpu_memory_limit: (int, default: `None`) maximum memory in MB to
allocate per GPU device.
:param allow_parallel_threads: (bool, default: `True`) allow TensorFlow
to use multithreading parallelism to improve performance at
the cost of determinism.
:param backend: (Union[Backend, str]) `Backend` or string name
of backend to use to execute preprocessing / training steps.
:param random_seed: (int: default: 42) random seed used for weights
initialization, splits and any other random function.
:param debug: (bool, default: `False) if `True` turns on `tfdbg` with
`inf_or_nan` checks.
# Return
:return: (List[dict]) List of results for each trial, ordered by
descending performance on the target metric.
"""
backend = initialize_backend(backend)
# check if config is a path or a dict
if isinstance(config, str): # assume path
with open_file(config, 'r') as def_file:
config_dict = yaml.safe_load(def_file)
else:
config_dict = config
# merge config with defaults
config = merge_with_defaults(config_dict)
if HYPEROPT not in config:
raise ValueError("Hyperopt Section not present in config")
hyperopt_config = config["hyperopt"]
update_hyperopt_params_with_defaults(hyperopt_config)
# print hyperopt config
logger.info(pformat(hyperopt_config, indent=4))
logger.info('\n')
sampler = hyperopt_config["sampler"]
executor = hyperopt_config["executor"]
parameters = hyperopt_config["parameters"]
split = hyperopt_config["split"]
output_feature = hyperopt_config["output_feature"]
metric = hyperopt_config["metric"]
goal = hyperopt_config["goal"]
######################
# check validity of output_feature / metric/ split combination
######################
if split == TRAINING:
if training_set is None and (
config['preprocessing']['split_probabilities'][0] <= 0):
raise ValueError(
'The data for the specified split for hyperopt "{}" '
'was not provided, '
'or the split amount specified in the preprocessing section '
'of the config is not greater than 0'.format(split))
elif split == VALIDATION:
if validation_set is None and (
config['preprocessing']['split_probabilities'][1] <= 0):
raise ValueError(
'The data for the specified split for hyperopt "{}" '
'was not provided, '
'or the split amount specified in the preprocessing section '
'of the config is not greater than 0'.format(split))
elif split == TEST:
if test_set is None and (
config['preprocessing']['split_probabilities'][2] <= 0):
raise ValueError(
'The data for the specified split for hyperopt "{}" '
'was not provided, '
'or the split amount specified in the preprocessing section '
'of the config is not greater than 0'.format(split))
else:
raise ValueError('unrecognized hyperopt split "{}". '
'Please provide one of: {}'.format(
split, {TRAINING, VALIDATION, TEST}))
if output_feature == COMBINED:
if metric != LOSS:
raise ValueError(
'The only valid metric for "combined" output feature is "loss"'
)
else:
output_feature_names = set(of['name']
for of in config['output_features'])
if output_feature not in output_feature_names:
raise ValueError('The output feature specified for hyperopt "{}" '
'cannot be found in the config. '
'Available ones are: {} and "combined"'.format(
output_feature, output_feature_names))
output_feature_type = None
for of in config['output_features']:
if of['name'] == output_feature:
output_feature_type = of[TYPE]
feature_class = get_from_registry(output_feature_type,
output_type_registry)
if metric not in feature_class.metric_functions:
# todo v0.4: allow users to specify also metrics from the overall
# and per class metrics from the trainign stats and in general
# and potprocessed metric
raise ValueError(
'The specified metric for hyperopt "{}" is not a valid metric '
'for the specified output feature "{}" of type "{}". '
'Available metrics are: {}'.format(
metric, output_feature, output_feature_type,
feature_class.metric_functions.keys()))
hyperopt_sampler = get_build_hyperopt_sampler(sampler[TYPE])(goal,
parameters,
**sampler)
hyperopt_executor = get_build_hyperopt_executor(executor[TYPE])(
hyperopt_sampler, output_feature, metric, split, **executor)
hyperopt_results = hyperopt_executor.execute(
config,
dataset=dataset,
training_set=training_set,
validation_set=validation_set,
test_set=test_set,
training_set_metadata=training_set_metadata,
data_format=data_format,
experiment_name=experiment_name,
model_name=model_name,
# model_load_path=None,
# model_resume_path=None,
skip_save_training_description=skip_save_training_description,
skip_save_training_statistics=skip_save_training_statistics,
skip_save_model=skip_save_model,
skip_save_progress=skip_save_progress,
skip_save_log=skip_save_log,
skip_save_processed_input=skip_save_processed_input,
skip_save_unprocessed_output=skip_save_unprocessed_output,
skip_save_predictions=skip_save_predictions,
skip_save_eval_stats=skip_save_eval_stats,
output_directory=output_directory,
gpus=gpus,
gpu_memory_limit=gpu_memory_limit,
allow_parallel_threads=allow_parallel_threads,
backend=backend,
random_seed=random_seed,
debug=debug,
**kwargs)
if backend.is_coordinator():
print_hyperopt_results(hyperopt_results)
if not skip_save_hyperopt_statistics:
makedirs(output_directory, exist_ok=True)
hyperopt_stats = {
'hyperopt_config':
hyperopt_config,
'hyperopt_results':
[t.to_dict() for t in hyperopt_results.ordered_trials],
}
save_hyperopt_stats(hyperopt_stats, output_directory)
logger.info('Hyperopt stats saved to: {}'.format(output_directory))
logger.info('Finished hyperopt')
return hyperopt_results
def hyperopt(
config: Union[str, dict],
dataset: Union[str, dict, pd.DataFrame] = None,
training_set: Union[str, dict, pd.DataFrame] = None,
validation_set: Union[str, dict, pd.DataFrame] = None,
test_set: Union[str, dict, pd.DataFrame] = None,
training_set_metadata: Union[str, dict] = None,
data_format: str = None,
experiment_name: str = "hyperopt",
model_name: str = "run",
resume: Optional[bool] = None,
skip_save_training_description: bool = False,
skip_save_training_statistics: bool = False,
skip_save_model: bool = False,
skip_save_progress: bool = False,
skip_save_log: bool = False,
skip_save_processed_input: bool = True,
skip_save_unprocessed_output: bool = False,
skip_save_predictions: bool = False,
skip_save_eval_stats: bool = False,
skip_save_hyperopt_statistics: bool = False,
output_directory: str = "results",
gpus: Union[str, int, List[int]] = None,
gpu_memory_limit: int = None,
allow_parallel_threads: bool = True,
callbacks: List[Callback] = None,
backend: Union[Backend, str] = None,
random_seed: int = default_random_seed,
hyperopt_log_verbosity: int = 3,
**kwargs,
) -> HyperoptResults:
"""This method performs an hyperparameter optimization.
# Inputs
:param config: (Union[str, dict]) config which defines
the different parameters of the model, features, preprocessing and
training. If `str`, filepath to yaml configuration file.
:param dataset: (Union[str, dict, pandas.DataFrame], default: `None`)
source containing the entire dataset to be used in the experiment.
If it has a split column, it will be used for splitting (0 for train,
1 for validation, 2 for test), otherwise the dataset will be
randomly split.
:param training_set: (Union[str, dict, pandas.DataFrame], default: `None`)
source containing training data.
:param validation_set: (Union[str, dict, pandas.DataFrame], default: `None`)
source containing validation data.
:param test_set: (Union[str, dict, pandas.DataFrame], default: `None`)
source containing test data.
:param training_set_metadata: (Union[str, dict], default: `None`)
metadata JSON file or loaded metadata. Intermediate preprocessed
structure containing the mappings of the input
dataset created the first time an input file is used in the same
directory with the same name and a '.meta.json' extension.
:param data_format: (str, default: `None`) format to interpret data
sources. Will be inferred automatically if not specified. Valid
formats are `'auto'`, `'csv'`, `'df'`, `'dict'`, `'excel'`, `'feather'`,
`'fwf'`, `'hdf5'` (cache file produced during previous training),
`'html'` (file containing a single HTML `<table>`), `'json'`, `'jsonl'`,
`'parquet'`, `'pickle'` (pickled Pandas DataFrame), `'sas'`, `'spss'`,
`'stata'`, `'tsv'`.
:param experiment_name: (str, default: `'experiment'`) name for
the experiment.
:param model_name: (str, default: `'run'`) name of the model that is
being used.
:param resume: (bool) If true, continue hyperopt from the state of the previous
run in the output directory with the same experiment name. If false, will create
new trials, ignoring any previous state, even if they exist in the output_directory.
By default, will attempt to resume if there is already an existing experiment with
the same name, and will create new trials if not.
:param skip_save_training_description: (bool, default: `False`) disables
saving the description JSON file.
:param skip_save_training_statistics: (bool, default: `False`) disables
saving training statistics JSON file.
:param skip_save_model: (bool, default: `False`) disables
saving model weights and hyperparameters each time the model
improves. By default Ludwig saves model weights after each epoch
the validation metric improves, but if the model is really big
that can be time consuming. If you do not want to keep
the weights and just find out what performance a model can get
with a set of hyperparameters, use this parameter to skip it,
but the model will not be loadable later on and the returned model
will have the weights obtained at the end of training, instead of
the weights of the epoch with the best validation performance.
:param skip_save_progress: (bool, default: `False`) disables saving
progress each epoch. By default Ludwig saves weights and stats
after each epoch for enabling resuming of training, but if
the model is really big that can be time consuming and will uses
twice as much space, use this parameter to skip it, but training
cannot be resumed later on.
:param skip_save_log: (bool, default: `False`) disables saving
TensorBoard logs. By default Ludwig saves logs for the TensorBoard,
but if it is not needed turning it off can slightly increase the
overall speed.
:param skip_save_processed_input: (bool, default: `False`) if input
dataset is provided it is preprocessed and cached by saving an HDF5
and JSON files to avoid running the preprocessing again. If this
parameter is `False`, the HDF5 and JSON file are not saved.
:param skip_save_unprocessed_output: (bool, default: `False`) by default
predictions and their probabilities are saved in both raw
unprocessed numpy files containing tensors and as postprocessed
CSV files (one for each output feature). If this parameter is True,
only the CSV ones are saved and the numpy ones are skipped.
:param skip_save_predictions: (bool, default: `False`) skips saving test
predictions CSV files.
:param skip_save_eval_stats: (bool, default: `False`) skips saving test
statistics JSON file.
:param skip_save_hyperopt_statistics: (bool, default: `False`) skips saving
hyperopt stats file.
:param output_directory: (str, default: `'results'`) the directory that
will contain the training statistics, TensorBoard logs, the saved
model and the training progress files.
:param gpus: (list, default: `None`) list of GPUs that are available
for training.
:param gpu_memory_limit: (int, default: `None`) maximum memory in MB to
allocate per GPU device.
:param allow_parallel_threads: (bool, default: `True`) allow TensorFlow
to use multithreading parallelism to improve performance at
the cost of determinism.
:param callbacks: (list, default: `None`) a list of
`ludwig.callbacks.Callback` objects that provide hooks into the
Ludwig pipeline.
:param backend: (Union[Backend, str]) `Backend` or string name
of backend to use to execute preprocessing / training steps.
:param random_seed: (int: default: 42) random seed used for weights
initialization, splits and any other random function.
:param hyperopt_log_verbosity: (int: default: 3) controls verbosity of
ray tune log messages. Valid values: 0 = silent, 1 = only status updates,
2 = status and brief trial results, 3 = status and detailed trial results.
# Return
:return: (List[dict]) List of results for each trial, ordered by
descending performance on the target metric.
"""
from ludwig.hyperopt.execution import get_build_hyperopt_executor, RayTuneExecutor
# check if config is a path or a dict
if isinstance(config, str): # assume path
with open_file(config, "r") as def_file:
config_dict = yaml.safe_load(def_file)
else:
config_dict = config
# Get mapping of input/output features that don't have an encoder for shared parameters
features_eligible_for_shared_params = {
INPUT_FEATURES:
get_features_eligible_for_shared_params(config_dict, INPUT_FEATURES),
OUTPUT_FEATURES:
get_features_eligible_for_shared_params(config_dict, OUTPUT_FEATURES),
}
# merge config with defaults
config = merge_with_defaults(config_dict)
if HYPEROPT not in config:
raise ValueError("Hyperopt Section not present in config")
hyperopt_config = config[HYPEROPT]
update_hyperopt_params_with_defaults(hyperopt_config)
# print hyperopt config
logging.info("Hyperopt config")
logging.info(pformat(hyperopt_config, indent=4))
logging.info("\n")
logging.info(
"Features that may be updated in hyperopt trials if default parameters are specified in the search space"
)
logging.info(pformat(dict(features_eligible_for_shared_params), indent=4))
logging.info("\n")
search_alg = hyperopt_config["search_alg"]
executor = hyperopt_config[EXECUTOR]
parameters = hyperopt_config["parameters"]
split = hyperopt_config["split"]
output_feature = hyperopt_config["output_feature"]
metric = hyperopt_config["metric"]
goal = hyperopt_config["goal"]
######################
# check validity of output_feature / metric/ split combination
######################
splitter = get_splitter(**config[PREPROCESSING]["split"])
if split == TRAINING:
if training_set is None and not splitter.has_split(0):
raise ValueError(
'The data for the specified split for hyperopt "{}" '
"was not provided, "
"or the split amount specified in the preprocessing section "
"of the config is not greater than 0".format(split))
elif split == VALIDATION:
if validation_set is None and not splitter.has_split(1):
raise ValueError(
'The data for the specified split for hyperopt "{}" '
"was not provided, "
"or the split amount specified in the preprocessing section "
"of the config is not greater than 0".format(split))
elif split == TEST:
if test_set is None and not splitter.has_split(2):
raise ValueError(
'The data for the specified split for hyperopt "{}" '
"was not provided, "
"or the split amount specified in the preprocessing section "
"of the config is not greater than 0".format(split))
else:
raise ValueError('unrecognized hyperopt split "{}". '
"Please provide one of: {}".format(
split, {TRAINING, VALIDATION, TEST}))
if output_feature == COMBINED:
if metric != LOSS:
raise ValueError(
'The only valid metric for "combined" output feature is "loss"'
)
else:
output_feature_names = {of[NAME] for of in config[OUTPUT_FEATURES]}
if output_feature not in output_feature_names:
raise ValueError('The output feature specified for hyperopt "{}" '
"cannot be found in the config. "
'Available ones are: {} and "combined"'.format(
output_feature, output_feature_names))
output_feature_type = None
for of in config[OUTPUT_FEATURES]:
if of[NAME] == output_feature:
output_feature_type = of[TYPE]
feature_class = get_from_registry(output_feature_type,
output_type_registry)
if metric not in feature_class.metric_functions:
# todo v0.4: allow users to specify also metrics from the overall
# and per class metrics from the trainign stats and in general
# and potprocessed metric
raise ValueError(
'The specified metric for hyperopt "{}" is not a valid metric '
'for the specified output feature "{}" of type "{}". '
"Available metrics are: {}".format(
metric, output_feature, output_feature_type,
feature_class.metric_functions.keys()))
hyperopt_executor = get_build_hyperopt_executor(executor[TYPE])(
parameters,
output_feature,
metric,
goal,
split,
search_alg=search_alg,
**executor)
# Explicitly default to a local backend to avoid picking up Ray or Horovod
# backend from the environment.
backend = backend or config_dict.get("backend") or "local"
backend = initialize_backend(backend)
if not (isinstance(backend, LocalBackend) or
(isinstance(hyperopt_executor, RayTuneExecutor)
and isinstance(backend, RayBackend))):
raise ValueError(
"Hyperopt requires using a `local` backend at this time, or "
"`ray` backend with `ray` executor.")
for callback in callbacks or []:
callback.on_hyperopt_init(experiment_name)
if not should_tune_preprocessing(config):
# preprocessing is not being tuned, so generate it once before starting trials
for callback in callbacks or []:
callback.on_hyperopt_preprocessing_start(experiment_name)
model = LudwigModel(
config=config,
backend=backend,
gpus=gpus,
gpu_memory_limit=gpu_memory_limit,
allow_parallel_threads=allow_parallel_threads,
callbacks=callbacks,
)
training_set, validation_set, test_set, training_set_metadata = model.preprocess(
dataset=dataset,
training_set=training_set,
validation_set=validation_set,
test_set=test_set,
training_set_metadata=training_set_metadata,
data_format=data_format,
skip_save_processed_input=skip_save_processed_input,
random_seed=random_seed,
)
dataset = None
for callback in callbacks or []:
callback.on_hyperopt_preprocessing_end(experiment_name)
for callback in callbacks or []:
callback.on_hyperopt_start(experiment_name)
hyperopt_results = hyperopt_executor.execute(
config,
dataset=dataset,
training_set=training_set,
validation_set=validation_set,
test_set=test_set,
training_set_metadata=training_set_metadata,
data_format=data_format,
experiment_name=experiment_name,
model_name=model_name,
resume=resume,
skip_save_training_description=skip_save_training_description,
skip_save_training_statistics=skip_save_training_statistics,
skip_save_model=skip_save_model,
skip_save_progress=skip_save_progress,
skip_save_log=skip_save_log,
skip_save_processed_input=skip_save_processed_input,
skip_save_unprocessed_output=skip_save_unprocessed_output,
skip_save_predictions=skip_save_predictions,
skip_save_eval_stats=skip_save_eval_stats,
output_directory=output_directory,
gpus=gpus,
gpu_memory_limit=gpu_memory_limit,
allow_parallel_threads=allow_parallel_threads,
callbacks=callbacks,
backend=backend,
random_seed=random_seed,
hyperopt_log_verbosity=hyperopt_log_verbosity,
features_eligible_for_shared_params=features_eligible_for_shared_params,
**kwargs,
)
if backend.is_coordinator():
print_hyperopt_results(hyperopt_results)
if not skip_save_hyperopt_statistics:
results_directory = os.path.join(output_directory, experiment_name)
makedirs(results_directory, exist_ok=True)
hyperopt_stats = {
"hyperopt_config":
hyperopt_config,
"hyperopt_results":
[t.to_dict() for t in hyperopt_results.ordered_trials],
}
save_hyperopt_stats(hyperopt_stats, results_directory)
logging.info(f"Hyperopt stats saved to: {results_directory}")
for callback in callbacks or []:
callback.on_hyperopt_end(experiment_name)
callback.on_hyperopt_finish(experiment_name)
logging.info("Finished hyperopt")
return hyperopt_results
