def interactions_data_frame(self) -> pd.DataFrame:
if not hasattr(self, "_interactions_data_frame"):
data = pd.concat(
[
pd.read_csv(self.train_data_frame_path),
pd.read_csv(self.val_data_frame_path),
],
ignore_index=True,
)
if self.sample_size > 0:
data = data[-self.sample_size:]
self._interactions_data_frame = preprocess_interactions_data_frame(
data,
self.project_config,
)
self._interactions_data_frame.sort_values(
self.project_config.timestamp_column_name).reset_index(
drop=True)
# Needed in case index_mapping was invoked before
if not hasattr(self, "_creating_index_mapping") and not hasattr(
self, "_interactions_data_frame_indexed"):
transform_with_indexing(self._interactions_data_frame,
self.index_mapping, self.project_config)
self._interactions_data_frame_indexed = True
return self._interactions_data_frame
def _direct_estimator_predict(self, df):
_df = preprocess_interactions_data_frame(
df.copy(), self.direct_estimator.project_config)
transform_with_indexing(_df, self.direct_estimator.index_mapping,
self.direct_estimator.project_config)
dataset = InteractionsDataset(
data_frame=_df,
embeddings_for_metadata=self.direct_estimator.
embeddings_for_metadata,
project_config=self.direct_estimator.project_config,
index_mapping=self.direct_estimator.index_mapping)
batch_sampler = FasterBatchSampler(dataset,
self.direct_estimator.batch_size,
shuffle=False)
data_loader = NoAutoCollationDataLoader(dataset,
batch_sampler=batch_sampler)
trial = (Trial(
self.direct_estimator.get_trained_module(),
criterion=lambda *args: torch.zeros(1,
device=self.direct_estimator.
torch_device,
requires_grad=True),
).with_generators(val_generator=data_loader).to(
self.direct_estimator.torch_device).eval())
with torch.no_grad():
rewards_tensor: torch.Tensor = trial.predict(
verbose=0, data_key=torchbearer.VALIDATION_DATA)
rewards: np.ndarray = rewards_tensor[:, 0].cpu().numpy()
return rewards
def run(self):
os.makedirs(self.output().path)
df: pd.DataFrame = pd.read_csv(self.input().path)
# Index test dataset
df['Index'] = df['SessionID']
print(df.head())
df = preprocess_interactions_data_frame(
df, self.model_training.project_config)
transform_with_indexing(df, self.model_training.index_mapping,
self.model_training.project_config)
df = df.sort_values("Index")
print(df.head())
print(df.shape)
generator = self.get_test_generator(df)
# Gente Model
model = self.model_training.get_trained_module()
model.to(self.torch_device)
model.eval()
scores = []
rank_list = []
#with Pool(os.cpu_count()) as p:
# list(tqdm(p.starmap(_get_rank_list, )))
reverse_index_mapping = self.model_training.reverse_index_mapping[
'ItemID']
reverse_index_mapping[1] = 0
# Inference
with torch.no_grad():
for i, (x, _) in tqdm(enumerate(generator), total=len(generator)):
input_params = x if isinstance(x, list) or isinstance(
x, tuple) else [x]
input_params = [t.to(self.torch_device) for t in input_params]
scores_tensor: torch.Tensor = model(*input_params)
scores_batch = scores_tensor.detach().cpu().numpy()
#scores.extend(scores_batch)
for score in tqdm(scores_batch, total=len(scores_batch)):
item_idx = np.argsort(score)[::-1][:10]
#from IPython import embed; embed()
item_id = [
int(reverse_index_mapping[item]) for item in item_idx
]
rank_list.append(item_id)
gc.collect()
np.savetxt(self.output().path +
'/submission_{}.csv'.format(self.task_name),
np.array(rank_list).astype(int),
fmt='%i',
delimiter=',')
def run(self):
os.makedirs(self.output().path)
df: pd.DataFrame = pd.read_parquet(self.input()[1].path)
df = preprocess_interactions_data_frame(
df, self.model_training.project_config)
data = SessionInteractionDataFrame(
item_column="",
normalize_dense_features=self.normalize_dense_features,
normalize_file_path=self.normalize_file_path)
data.transform_data_frame(df, "TEST_GENERATOR")
# Index test dataset
df['Index'] = df['SessionID'].astype(int)
df = df.sort_values("Index")
df.to_csv(self.output().path + "/dataset.csv")
transform_with_indexing(df, self.model_training.index_mapping,
self.model_training.project_config)
#
df.to_csv(self.output().path + "/dataset_indexed.csv")
generator = self.get_test_generator(df)
print(df.head())
print(df.shape)
reverse_index_mapping = self.model_training.reverse_index_mapping[
'ItemID']
reverse_index_mapping[1] = 0
if self.model_eval == "model":
rank_list = self.model_rank_list(generator, reverse_index_mapping)
elif self.model_eval == "most_popular":
rank_list = self.most_popular_rank_list(generator,
reverse_index_mapping)
elif self.model_eval == "coocorrence":
rank_list = self.coocorrence_rank_list(generator,
reverse_index_mapping)
df_moda = self.pos_process(rank_list)
rank_list = df_moda['reclist_2'].values
rank_list = np.array([np.array(r).astype(int) for r in rank_list])
#
df_moda.to_csv(self.output().path + '/df_submission.csv', index=False)
np.savetxt(self.output().path +
'/submission_{}.csv'.format(self.task_name),
rank_list,
fmt='%i',
delimiter=',')
def test_data_frame(self) -> pd.DataFrame:
if not hasattr(self, "_test_data_frame"):
print("test_data_frame:")
self._test_data_frame = preprocess_interactions_data_frame(
pd.read_csv(self.test_data_frame_path,
usecols=self.dataset_read_columns),
self.project_config)
transform_with_indexing(self._test_data_frame, self.index_mapping,
self.project_config)
return self._test_data_frame
def val_data_frame(self) -> pd.DataFrame:
if not hasattr(self, "_val_data_frame"):
print("val_data_frame:")
self._val_data_frame = preprocess_interactions_data_frame(
pd.read_csv(
self.val_data_frame_path,
usecols=[c.name for c in self.project_config.all_columns]),
self.project_config)
transform_with_indexing(self._val_data_frame, self.index_mapping,
self.project_config)
return self._val_data_frame
def metadata_data_frame(self) -> Optional[pd.DataFrame]:
if not hasattr(self, "_metadata_data_frame"):
self._metadata_data_frame = (pd.read_csv(
self.metadata_data_frame_path) if self.metadata_data_frame_path
else None)
if self._metadata_data_frame is not None:
literal_eval_array_columns(
self._metadata_data_frame,
self.project_config.metadata_columns)
#
transform_with_indexing(self._metadata_data_frame,
self.index_mapping, self.project_config)
return self._metadata_data_frame
def test_data_frame(self) -> pd.DataFrame:
if not hasattr(self, "_test_data_frame"):
self._test_data_frame = preprocess_interactions_data_frame(
pd.read_csv(self.test_data_frame_path), self.project_config
)
# Needed in case index_mapping was invoked before
if not hasattr(self, "_creating_index_mapping") and not hasattr(
self, "_test_data_frame_indexed"
):
transform_with_indexing(
self._test_data_frame, self.index_mapping, self.project_config
)
self._test_data_frame_indexed = True
return self._test_data_frame
def fill_ps(self, df: pd.DataFrame, pool: Pool):
policy_estimator_df = preprocess_interactions_data_frame(df.copy(), self.policy_estimator.project_config)
transform_with_indexing(
policy_estimator_df,
self.policy_estimator.index_mapping,
self.policy_estimator.project_config,
)
if self.available_arms_column:
policy_estimator_df[self.available_arms_column] = policy_estimator_df[
self.available_arms_column
].map(
functools.partial(
map_array,
mapping=self.policy_estimator.index_mapping[
self.policy_estimator.project_config.item_column.name
],
)
)
dataset = InteractionsDataset(
data_frame=policy_estimator_df,
embeddings_for_metadata=self.policy_estimator.embeddings_for_metadata,
project_config=self.policy_estimator.project_config,
index_mapping=self.policy_estimator.index_mapping
)
batch_sampler = FasterBatchSampler(
dataset, self.policy_estimator.batch_size, shuffle=False
)
data_loader = NoAutoCollationDataLoader(dataset, batch_sampler=batch_sampler)
#from IPython import embed
#embed()
trial = (
Trial(
self.policy_estimator.get_trained_module(),
criterion=lambda *args: torch.zeros(
1, device=self.policy_estimator.torch_device, requires_grad=True
),
)
.with_generators(val_generator=data_loader)
.to(self.policy_estimator.torch_device)
.eval()
)
with torch.no_grad():
log_probas: torch.Tensor = trial.predict(
verbose=0, data_key=torchbearer.VALIDATION_DATA
)
probas: np.ndarray = torch.exp(log_probas).cpu().numpy()
item_indices = policy_estimator_df[self.item_column]
params = (
zip(item_indices, probas, policy_estimator_df[self.available_arms_column])
if self.available_arms_column
else zip(item_indices, probas)
)
# from IPython import embed; embed()
df[self.propensity_score_column] = list(
tqdm(pool.starmap(_get_ps_from_probas, params), total=len(df))
)
