def train(working_dir, dataset='56_sunspots', epochs=100, stopping_patience=3):
# read in training df
df = pd.read_csv(
f'../../datasets/seed_datasets_current/{dataset}/TRAIN/dataset_TRAIN/tables/learningData.csv'
)
df = _multi_index_prep(df, dataset)
df = _time_col_to_seconds(df, dataset)
# create TimeSeries and Learner objects
ds = _create_ts_object(df, dataset)
learner = DeepARLearner(ds, verbose=1)
# fit
learner.fit(epochs=epochs,
stopping_patience=stopping_patience,
early_stopping=True,
checkpoint_dir=os.path.join("./checkpoints", working_dir))
# evaluate
test_df = pd.read_csv(
f'../../datasets/seed_datasets_current/{dataset}/TEST/dataset_TEST/tables/learningData.csv'
)
test_df = _multi_index_prep(test_df, dataset)
test_df = _time_col_to_seconds(test_df, dataset)
test_ds = _create_ts_test_object(test_df, ds, dataset)
preds = learner.predict(test_ds,
horizon=None,
samples=1,
include_all_training=True).reshape(-1)
def _create_data_object_and_learner(self, val_split):
""" private util function:
creates (or updates) train ds object and learner
Arguments:
val_split {float} -- proportion of training data to withhold for validation
"""
# Create TimeSeries dataset objects
#logger.info(self._ts_frame.head())
self._ts_object = TimeSeriesTrain(
self._ts_frame,
target_idx=self._target_column,
timestamp_idx=self._timestamp_column,
grouping_idx=self._grouping_column,
index_col=self._index_column,
count_data=self._count_data,
negative_obs=self.hyperparams["negative_obs"],
val_split=val_split,
integer_timestamps=self._integer_timestamps,
freq=self.freq,
)
#logger.info(self._ts_object.data.head())
# Create learner
self._learner = DeepARLearner(
self._ts_object,
emb_dim=self.hyperparams["emb_dim"],
lstm_dim=self.hyperparams["lstm_dim"],
dropout=self.hyperparams["dropout_rate"],
lr=self.hyperparams["learning_rate"],
batch_size=self.hyperparams["batch_size"],
train_window=self.hyperparams["window_size"],
verbose=0,
)
# save weights so we can restart fitting from scratch (if desired by caller)
self._learner.save_weights("model_initial_weights.h5")
def produce(self,
*,
inputs: Inputs,
timeout: float = None,
iterations: int = None) -> CallResult[Outputs]:
""" Produce primitive's predictions for specific time series at specific future time instances
* these specific timesteps / series are specified implicitly by input dataset
Arguments:
inputs {Inputs} -- full D3M dataframe, containing attributes, key, and target
Keyword Arguments:
timeout {float} -- timeout, not considered (default: {None})
iterations {int} -- iterations, not considered (default: {None})
Raises:
PrimitiveNotFittedError: if primitive not fit
Returns:
CallResult[Outputs] -- (N, 2) dataframe with d3m_index and value for each prediction slice requested.
prediction slice = specific horizon idx for specific series in specific regression
"""
if not self._is_fit:
raise PrimitiveNotFittedError("Primitive not fitted.")
if len(self._drop_cols_no_tgt
) > 0 and inputs.shape[1] != self._cols_after_drop:
test_frame = inputs.remove_columns(self._drop_cols_no_tgt)
else:
test_frame = inputs.copy()
# Create TimeSeriesTest object
if self._train_data.equals(inputs):
ts_test_object = TimeSeriesTest(self._ts_object)
# test
else:
ts_test_object = TimeSeriesTest(self._ts_object, test_frame)
# get prediction slices
pred_intervals = self._get_pred_intervals(test_frame)
# make predictions with learner
learner = DeepARLearner(
self._ts_object,
emb_dim=self.hyperparams["emb_dim"],
lstm_dim=self.hyperparams["lstm_dim"],
dropout=self.hyperparams["dropout_rate"],
lr=self.hyperparams["learning_rate"],
batch_size=self.hyperparams["batch_size"],
train_window=self.hyperparams["window_size"],
verbose=0,
)
learner.load_weights(self.hyperparams['weights_filepath'])
start_time = time.time()
logger.info(f"Making predictions...")
preds = learner.predict(ts_test_object, include_all_training=True)
logger.info(
f"Prediction took {time.time() - start_time}s. Predictions array shape: {preds.shape}"
)
# slice predictions with learned intervals
all_preds = []
for p, idxs in zip(preds, pred_intervals.values):
# all_preds.extend(p[: len(idxs)]) # this takes first n predictions
all_preds.extend([p[i] for i in idxs
]) # this takes predictions at actual indices
flat_list = np.array([p for pred_list in all_preds for p in pred_list])
# if np.isinf(all_preds).any():
# logger.debug(f'There are {np.isinf(all_preds).sum()} inf preds')
# if np.isnan(all_preds).any():
# logger.debug(f'There are {np.isnan(all_preds).sum()} nan preds')
# logger.debug(f'Max: {preds.max()}, Min: {preds.min()}')
# fill nans with 0s in case model predicted some (shouldnt need to - preventing edge case)
flat_list = np.nan_to_num(flat_list)
# create output frame
result_df = container.DataFrame(
{self._ts_frame.columns[self._target_column]: flat_list},
generate_metadata=True,
)
result_df.metadata = result_df.metadata.add_semantic_type(
(metadata_base.ALL_ELEMENTS, 0),
("https://metadata.datadrivendiscovery.org/types/PredictedTarget"),
)
return CallResult(result_df, has_finished=self._is_fit)
class DeepArPrimitive(SupervisedLearnerPrimitiveBase[Inputs, Outputs, Params,
Hyperparams]):
"""
Primitive that applies a deep autoregressive forecasting algorithm for time series
prediction. The implementation is based off of this paper: https://arxiv.org/pdf/1704.04110.pdf
and is implemented in AWS's Sagemaker interface.
Training inputs: 1) Feature dataframe, 2) Target dataframe
Outputs: Dataframe with predictions for specific time series at specific future time instances
Arguments:
hyperparams {Hyperparams} -- D3M Hyperparameter object
Keyword Arguments:
random_seed {int} -- random seed (default: {0})
"""
metadata = metadata_base.PrimitiveMetadata({
# Simply an UUID generated once and fixed forever. Generated using "uuid.uuid4()".
"id":
"3410d709-0a13-4187-a1cb-159dd24b584b",
"version":
__version__,
"name":
"DeepAR",
# Keywords do not have a controlled vocabulary. Authors can put here whatever they find suitable.
"keywords": [
"time series",
"forecasting",
"recurrent neural network",
"autoregressive",
],
"source": {
"name":
__author__,
"contact":
__contact__,
"uris": [
# Unstructured URIs.
"https://github.com/Yonder-OSS/D3M-Primitives",
],
},
# A list of dependencies in order. These can be Python packages, system packages, or Docker images.
# Of course Python packages can also have their own dependencies, but sometimes it is necessary to
# install a Python package first to be even able to run setup.py of another package. Or you have
# a dependency which is not on PyPi.
"installation": [
{
"type": "PIP",
"package": "cython",
"version": "0.29.14"
},
{
"type":
metadata_base.PrimitiveInstallationType.PIP,
"package_uri":
"git+https://github.com/Yonder-OSS/D3M-Primitives.git@{git_commit}#egg=yonder-primitives"
.format(git_commit=utils.current_git_commit(
os.path.dirname(__file__)), ),
},
],
# The same path the primitive is registered with entry points in setup.py.
"python_path":
"d3m.primitives.time_series_forecasting.lstm.DeepAR",
# Choose these from a controlled vocabulary in the schema. If anything is missing which would
# best describe the primitive, make a merge request.
"algorithm_types": [
metadata_base.PrimitiveAlgorithmType.RECURRENT_NEURAL_NETWORK,
],
"primitive_family":
metadata_base.PrimitiveFamily.TIME_SERIES_FORECASTING,
})
def __init__(self,
*,
hyperparams: Hyperparams,
random_seed: int = 0) -> None:
super().__init__(hyperparams=hyperparams, random_seed=random_seed)
# set seed for reproducibility
tf.random.set_seed(random_seed)
self._cols_after_drop = 0
self._is_fit = False
def get_params(self) -> Params:
if not self._is_fit:
return Params(drop_cols_no_tgt=None,
cols_after_drop=None,
train_data=None,
ts_frame=None,
target_column=None,
timestamp_column=None,
ts_object=None,
grouping_column=None,
output_columns=None,
min_train=None,
freq=None,
integer_timestamps=None,
is_fit=None)
return Params(drop_cols_no_tgt=self._drop_cols_no_tgt,
cols_after_drop=self._cols_after_drop,
train_data=self._train_data,
ts_frame=self._ts_frame,
target_column=self._target_column,
timestamp_column=self._timestamp_column,
ts_object=self._ts_object,
grouping_column=self._grouping_column,
output_columns=self._output_columns,
min_train=self._min_train,
freq=self.freq,
integer_timestamps=self._integer_timestamps,
is_fit=self._is_fit)
def set_params(self, *, params: Params) -> None:
self._drop_cols_no_tgt = params['drop_cols_no_tgt']
self._cols_after_drop = params['cols_after_drop']
self._train_data = params['train_data']
self._ts_frame = params['ts_frame']
self._target_column = params['target_column']
self._timestamp_column = params['timestamp_column']
self._ts_object = params['ts_object']
self._grouping_column = params['grouping_column']
self._output_columns = params['output_columns']
self._min_train = params['min_train']
self.freq = params['freq']
self._integer_timestamps = params['integer_timestamps']
self._is_fit = params['is_fit']
def _drop_multiple_special_cols(self, col_list, col_type):
"""
private util function that creates list of duplicated special columns (for deletion)
Arguments:
col_list {List[int]} -- list of column indices
col_type {str} -- D3M semantic type
Returns:
int or None -- first column idx in col_list if any column idxs are marked (else None)
"""
if len(col_list) == 0:
return None
elif len(col_list) > 1:
logger.warn(
f"""There are more than one {col_type} marked. This primitive will use the first and drop other {col_type}s."""
)
self._drop_cols += col_list[1:]
if col_type != "target column":
self._drop_cols_no_tgt += col_list[1:]
return col_list[0]
def _get_cols(self, input_metadata):
""" private util function: get indices of important columns from metadata
Arguments:
input_metadata {D3M Metadata object} -- D3M Metadata object for input frame
Raises:
ValueError: If Target column is not of type 'Integer' or 'Float'
"""
self._drop_cols = []
self._drop_cols_no_tgt = []
# get target idx (first column by default)
target_columns = input_metadata.list_columns_with_semantic_types((
"https://metadata.datadrivendiscovery.org/types/SuggestedTarget",
"https://metadata.datadrivendiscovery.org/types/TrueTarget",
"https://metadata.datadrivendiscovery.org/types/Target",
))
if len(target_columns) == 0:
raise ValueError("At least one column must be marked as a target")
self._target_column = self._drop_multiple_special_cols(
target_columns, "target column")
# get timestamp idx (first column by default)
timestamp_columns = input_metadata.list_columns_with_semantic_types((
"https://metadata.datadrivendiscovery.org/types/Time",
"http://schema.org/DateTime",
))
self._timestamp_column = self._drop_multiple_special_cols(
timestamp_columns, "timestamp column")
# get grouping idx and add suggested grouping keys to drop_cols list
grouping_columns = input_metadata.list_columns_with_semantic_types(
("https://metadata.datadrivendiscovery.org/types/GroupingKey", ))
self._grouping_column = self._drop_multiple_special_cols(
grouping_columns, "grouping column")
suggested_grouping_columns = input_metadata.list_columns_with_semantic_types(
("https://metadata.datadrivendiscovery.org/types/SuggestedGroupingKey",
))
self._drop_cols += suggested_grouping_columns
self._drop_cols_no_tgt += suggested_grouping_columns
# get index_col (first index column by default)
index_columns = input_metadata.list_columns_with_semantic_types(
("https://metadata.datadrivendiscovery.org/types/PrimaryKey", ))
self._index_column = self._drop_multiple_special_cols(
index_columns, "index column")
# determine whether targets are count data
target_semantic_types = input_metadata.query_column_field(
self._target_column, "semantic_types")
if self.hyperparams["count_data"] is not None:
self._count_data = self.hyperparams["count_data"]
elif "http://schema.org/Integer" in target_semantic_types:
if np.min(self._ts_frame.iloc[:, self._target_column]) > 0:
self._count_data = True
else:
self._count_data = False
elif "http://schema.org/Float" in target_semantic_types:
self._count_data = False
else:
raise ValueError(
"Target column is not of type 'Integer' or 'Float'")
def _update_indices(self):
""" private util function:
subtract length of drop cols from each marked idx to account for smaller df
"""
length = len(self._drop_cols)
if self._target_column is not None:
self._target_column -= length
if self._timestamp_column is not None:
self._timestamp_column -= length
if self._grouping_column is not None:
self._grouping_column -= length
if self._index_column is not None:
self._index_column -= length
self._cols_after_drop = self._ts_frame.shape[0]
def _create_data_object_and_learner(self, val_split):
""" private util function:
creates (or updates) train ds object and learner
Arguments:
val_split {float} -- proportion of training data to withhold for validation
"""
# Create TimeSeries dataset objects
self._ts_object = TimeSeriesTrain(
self._ts_frame,
target_idx=self._target_column,
timestamp_idx=self._timestamp_column,
grouping_idx=self._grouping_column,
index_col=self._index_column,
count_data=self._count_data,
negative_obs=self.hyperparams["negative_obs"],
val_split=val_split,
integer_timestamps=self._integer_timestamps,
freq=self.freq,
)
# Create learner
self._learner = DeepARLearner(
self._ts_object,
emb_dim=self.hyperparams["emb_dim"],
lstm_dim=self.hyperparams["lstm_dim"],
dropout=self.hyperparams["dropout_rate"],
lr=self.hyperparams["learning_rate"],
batch_size=self.hyperparams["batch_size"],
train_window=self.hyperparams["window_size"],
verbose=0,
)
# save weights so we can restart fitting from scratch (if desired by caller)
self._learner.save_weights(self.hyperparams['weights_filepath'])
def set_training_data(self, *, inputs: Inputs, outputs: Outputs) -> None:
""" Sets primitive's training data
Arguments:
inputs {Inputs} -- D3M dataframe containing attributes
outputs {Outputs} -- D3M dataframe containing targets
Raises:
ValueError: If multiple columns are annotated with 'Time' or 'DateTime' metadata
"""
# save copy of train data so we don't predict for each row in training
self._output_columns = outputs.columns
self._train_data = inputs.copy()
# combine inputs and outputs for internal TimeSeries object
self._ts_frame = inputs.append_columns(outputs)
# Parse cols needed for ts object
self._get_cols(self._ts_frame.metadata)
# drop cols if multiple special type columns
if len(self._drop_cols) > 0:
self._ts_frame = self._ts_frame.remove_columns(self._drop_cols)
self._update_indices()
# assumption is that integer timestamps are days (treated this way by DeepAR objects)
if "http://schema.org/Integer" in self._ts_frame.metadata.query_column_field(
self._timestamp_column, "semantic_types"):
self._integer_timestamps = True
else:
self._integer_timestamps = False
# calculate frequency of time series
g_col, t_col = (
self._ts_frame.columns[self._grouping_column],
self._ts_frame.columns[self._timestamp_column],
)
if self._grouping_column is None:
time_col_sorted = np.sort(self._ts_frame[t_col])
self._min_train = time_col_sorted[0]
self.freq = calculate_time_frequency(time_col_sorted[1] -
self._min_train)
# self._train_diff = int(
# np.diff(np.sort(self._ts_frame.iloc[:, self._timestamp_column]))[0]
# )
else:
# assume frequency is the same across all time series
self.freq = calculate_time_frequency(
int(
self._ts_frame.groupby(g_col)[t_col].apply(
lambda x: np.diff(np.sort(x))).iloc[0][0]))
self._min_train = self._ts_frame.groupby(g_col)[t_col].agg(
"min").min()
# Create TimeSeries dataset object and learner
self._create_data_object_and_learner(self.hyperparams["val_split"])
def fit(self,
*,
timeout: float = None,
iterations: int = None) -> CallResult[None]:
""" Fits DeepAR model using training data from set_training_data and hyperparameters
Keyword Arguments:
timeout {float} -- timeout, considered (default: {None})
iterations {int} -- iterations, considered (default: {None})
Returns:
CallResult[None]
"""
# special case for no validation
if iterations is not None:
self._create_data_object_and_learner(0)
if iterations is None:
iterations_set = False
iterations = self.hyperparams["epochs"]
validation = self.hyperparams["val_split"] > 0
else:
iterations_set = True
validation = False
# time training for 1 epoch so we can consider timeout argument thoughtfully
if timeout:
logger.info("""Timing the fitting procedure for one epoch so we
can consider timeout thoughtfully""")
start_time = time.time()
_, iterations_completed = self._learner.fit(
validation=validation,
steps_per_epoch=self.hyperparams["steps_per_epoch"],
epochs=1,
stopping_patience=self.hyperparams["early_stopping_patience"],
stopping_delta=self.hyperparams["early_stopping_delta"],
tensorboard=False,
)
epoch_time_estimate = time.time() - start_time
# subract 1 for epoch that already happened and 1 more to be safe
timeout_epochs = timeout // epoch_time_estimate - 2
iters = min(timeout_epochs, iterations)
else:
iters = iterations
# normal fitting
logger.info(f"Fitting for {iters} iterations")
start_time = time.time()
_, iterations_completed = self._learner.fit(
validation=validation,
steps_per_epoch=self.hyperparams["steps_per_epoch"],
epochs=iters,
stopping_patience=self.hyperparams["early_stopping_patience"],
stopping_delta=self.hyperparams["early_stopping_delta"],
tensorboard=False,
)
logger.info(
f"Fit for {iterations_completed} epochs, took {time.time() - start_time}s"
)
# maintain primitive state (mark that training data has been used)
self._new_train_data = False
self._is_fit = True
# use fitting history to set CallResult return values
if iterations_set:
has_finished = False
elif iters < iterations:
has_finished = False
else:
has_finished = self._is_fit
return CallResult(None,
has_finished=has_finished,
iterations_done=iterations_completed)
def _get_pred_intervals(self, df, keep_all=False):
""" private util function that retrieves unevenly spaced prediction intervals from data frame
Arguments:
df {pandas df} -- df of predictions from which to extract prediction intervals
Keyword Arguments:
keep_all {bool} -- if True, take every interval slice, otherwise only take
those given by the df
Returns:
pd Series -- series of intervals, indexed by group, granularity of 1 interval
"""
# no grouping column
if self._grouping_column is None:
interval = discretize_time_difference(
df.iloc[:, self._timestamp_column],
self._min_train,
self.freq,
self._integer_timestamps,
)
if keep_all:
interval = np.arange(min(interval), max(interval) + 1)
return pd.Series([interval])
# grouping column
else:
g_col, t_col = (
df.columns[self._grouping_column],
df.columns[self._timestamp_column],
)
all_intervals, groups = [], []
for (group, vals) in df.groupby(g_col)[t_col]:
interval = discretize_time_difference(vals, self._min_train,
self.freq,
self._integer_timestamps)
if keep_all:
interval = np.arange(min(interval), max(interval) + 1)
all_intervals.append(interval)
groups.append(group)
return pd.Series(all_intervals, index=groups)
def produce(self,
*,
inputs: Inputs,
timeout: float = None,
iterations: int = None) -> CallResult[Outputs]:
""" Produce primitive's predictions for specific time series at specific future time instances
* these specific timesteps / series are specified implicitly by input dataset
Arguments:
inputs {Inputs} -- full D3M dataframe, containing attributes, key, and target
Keyword Arguments:
timeout {float} -- timeout, not considered (default: {None})
iterations {int} -- iterations, not considered (default: {None})
Raises:
PrimitiveNotFittedError: if primitive not fit
Returns:
CallResult[Outputs] -- (N, 2) dataframe with d3m_index and value for each prediction slice requested.
prediction slice = specific horizon idx for specific series in specific regression
"""
if not self._is_fit:
raise PrimitiveNotFittedError("Primitive not fitted.")
if len(self._drop_cols_no_tgt
) > 0 and inputs.shape[1] != self._cols_after_drop:
test_frame = inputs.remove_columns(self._drop_cols_no_tgt)
else:
test_frame = inputs.copy()
# Create TimeSeriesTest object
if self._train_data.equals(inputs):
ts_test_object = TimeSeriesTest(self._ts_object)
# test
else:
ts_test_object = TimeSeriesTest(self._ts_object, test_frame)
# get prediction slices
pred_intervals = self._get_pred_intervals(test_frame)
# make predictions with learner
learner = DeepARLearner(
self._ts_object,
emb_dim=self.hyperparams["emb_dim"],
lstm_dim=self.hyperparams["lstm_dim"],
dropout=self.hyperparams["dropout_rate"],
lr=self.hyperparams["learning_rate"],
batch_size=self.hyperparams["batch_size"],
train_window=self.hyperparams["window_size"],
verbose=0,
)
learner.load_weights(self.hyperparams['weights_filepath'])
start_time = time.time()
logger.info(f"Making predictions...")
preds = learner.predict(ts_test_object, include_all_training=True)
logger.info(
f"Prediction took {time.time() - start_time}s. Predictions array shape: {preds.shape}"
)
# slice predictions with learned intervals
all_preds = []
for p, idxs in zip(preds, pred_intervals.values):
# all_preds.extend(p[: len(idxs)]) # this takes first n predictions
all_preds.extend([p[i] for i in idxs
]) # this takes predictions at actual indices
flat_list = np.array([p for pred_list in all_preds for p in pred_list])
# if np.isinf(all_preds).any():
# logger.debug(f'There are {np.isinf(all_preds).sum()} inf preds')
# if np.isnan(all_preds).any():
# logger.debug(f'There are {np.isnan(all_preds).sum()} nan preds')
# logger.debug(f'Max: {preds.max()}, Min: {preds.min()}')
# fill nans with 0s in case model predicted some (shouldnt need to - preventing edge case)
flat_list = np.nan_to_num(flat_list)
# create output frame
result_df = container.DataFrame(
{self._ts_frame.columns[self._target_column]: flat_list},
generate_metadata=True,
)
result_df.metadata = result_df.metadata.add_semantic_type(
(metadata_base.ALL_ELEMENTS, 0),
("https://metadata.datadrivendiscovery.org/types/PredictedTarget"),
)
return CallResult(result_df, has_finished=self._is_fit)
def produce_confidence_intervals(self,
*,
inputs: Inputs,
timeout: float = None,
iterations: int = None
) -> CallResult[Outputs]:
""" produce confidence intervals for each series 'confidence_interval_horizon' periods into
the future
Arguments:
inputs {Inputs} -- full D3M dataframe, containing attributes, key, and target
Keyword Arguments:
timeout {float} -- timeout, not considered (default: {None})
iterations {int} -- iterations, considered (default: {None})
Raises:
PrimitiveNotFittedError:
Returns:
CallResult[Outputs] --
Ex.
series | timestep | mean | 0.05 | 0.95
--------------------------------------
a | 0 | 5 | 3 | 7
a | 1 | 6 | 4 | 8
b | 0 | 5 | 3 | 7
b | 1 | 6 | 4 | 8
"""
if not self._is_fit:
raise PrimitiveNotFittedError("Primitive not fitted.")
alpha = self.hyperparams["confidence_interval_alpha"]
if len(self._drop_cols_no_tgt
) > 0 and inputs.shape[1] != self._cols_after_drop:
test_frame = inputs.remove_columns(self._drop_cols_no_tgt)
else:
test_frame = inputs.copy()
# Create TimeSeriesTest object
if self._train_data.equals(inputs):
ts_test_object = TimeSeriesTest(self._ts_object)
include_all_training = True
horizon = 0
# test
else:
ts_test_object = TimeSeriesTest(self._ts_object, test_frame)
include_all_training = self.hyperparams[
'seed_predictions_with_all_data']
horizon = self.hyperparams["confidence_interval_horizon"]
# make predictions with learner
start_time = time.time()
logger.info(f"Making predictions...")
preds = self._learner.predict(
ts_test_object,
horizon=horizon,
samples=self.hyperparams["confidence_interval_samples"],
include_all_training=include_all_training,
point_estimate=False)
logger.info(
f"Prediction took {time.time() - start_time}s. Predictions array shape: {preds.shape}"
)
# convert samples to percentiles
means = np.percentile(preds, 50, axis=2).reshape(-1, 1)
lowers = np.percentile(preds, alpha / 2 * 100, axis=2).reshape(-1, 1)
uppers = np.percentile(preds, (1 - alpha / 2) * 100,
axis=2).reshape(-1, 1)
assert (lowers < means).all()
assert (means < uppers).all()
# convert to df
if self._grouping_column is None:
indices = np.repeat(self._output_columns[0], preds.shape[1])
else:
indices = np.repeat(
test_frame[test_frame.columns[self._grouping_column]].unique(),
preds.shape[1])
interval_df = pd.DataFrame(
np.concatenate((means, lowers, uppers), axis=1),
columns=["mean", str(alpha / 2),
str(1 - alpha / 2)],
index=indices,
)
# add index column
interval_df["horizon_index"] = np.tile(np.arange(preds.shape[1]),
len(interval_df.index.unique()))
logger.debug(interval_df.head())
# structure return df
return CallResult(
container.DataFrame(interval_df, generate_metadata=True),
has_finished=self._is_fit,
)
def hp_search(working_dir,
dataset='56_sunspots',
epochs=100,
metric='eval_mae_result',
stopping_patience=5,
stopping_delta=1):
working_dir = os.path.join("./checkpoints", working_dir)
# define domains for HP search
HP_EMB_DIM = hp.HParam('emb_dim', hp.Discrete([32, 64, 128]))
HP_LSTM_DIM = hp.HParam('lstm_dim', hp.Discrete([32, 64, 128]))
HP_DROPOUT = hp.HParam('lstm_dropout', hp.Discrete([0.1, 0.2, 0.3]))
HP_LR = hp.HParam('learning_rate', hp.Discrete([.0001, .001, .01]))
HP_BS = hp.HParam('batch_size', hp.Discrete([32, 64, 128]))
HP_WINDOW = hp.HParam('window_size', hp.Discrete([20, 40, 60]))
# set up config
with tf.summary.create_file_writer(working_dir).as_default():
hp.hparams_config(hparams=[
HP_EMB_DIM, HP_LSTM_DIM, HP_DROPOUT, HP_LR, HP_BS, HP_WINDOW
],
metrics=[hp.Metric(metric, display_name=metric)])
# read in training df
df = pd.read_csv(
f'../../datasets/seed_datasets_current/{dataset}/TRAIN/dataset_TRAIN/tables/learningData.csv'
)
df = _multi_index_prep(df, dataset)
df = _time_col_to_seconds(df, dataset)
# create TimeSeries and Learner objects
ds = _create_ts_object(df, dataset)
# grid search over parameters
run_num = 0
total_run_count = len(HP_EMB_DIM.domain.values) * \
len(HP_LSTM_DIM.domain.values) * \
len(HP_DROPOUT.domain.values) * \
len(HP_LR.domain.values) * \
len(HP_BS.domain.values) * \
len(HP_WINDOW.domain.values)
# outfile for saving hp config and runtimes
outfile = open(os.path.join(working_dir, "metrics.txt"), "w+", buffering=1)
for emb_dim in HP_EMB_DIM.domain.values:
for lstm_dim in HP_LSTM_DIM.domain.values:
for dropout in HP_DROPOUT.domain.values:
for lr in HP_LR.domain.values:
for bs in HP_BS.domain.values:
for window_size in HP_WINDOW.domain.values:
# create dict of parameters
hp_dict = {
'emb_dim': emb_dim,
'lstm_dim': lstm_dim,
'lstm_dropout': dropout,
'learning_rate': lr,
'batch_size': bs,
'window_size': window_size,
}
run_name = f'run-{run_num}'
logger.info(
f'--- Starting Run: {run_name} of {total_run_count} ---'
)
# print_dict = {
# h.name: hp_dict[h] for h in hp_dict
# }
logger.info(f'HP Dict: {hp_dict}')
hp.hparams(hp_dict)
# create learner and fit with these HPs
start_time = time.time()
learner = DeepARLearner(ds,
verbose=1,
hparams=hp_dict)
final_metric = learner.fit(
epochs=epochs,
stopping_patience=stopping_patience,
stopping_delta=stopping_delta,
checkpoint_dir=os.path.join(
working_dir, run_name))
outfile.write(
f'HPs: {hp_dict} ---- Metric: {final_metric} ---- Time: {round(time.time() - start_time,2)}\n'
)
tf.summary.scalar(metric, final_metric, step=1)
run_num += 1
outfile.close()