PARModel validating arguments in README example

[sarahmish]

Environment Details

Please indicate the following details about the environment in which you found the bug:

• DeepEcho version: 0.2.0
• Python version: 3.7
• Operating System: macOS 10.15.7

Error Description

I tried to run the quickstart example in the README.md but it does not work on the PARModel on pytorch==1.9; the BasicGANModel works fine.
It seems like dist.log_prob is validating the argument by default which in negative cases is invalid. In previous versions (pytorch==1.7) this wasn't the case and it will return None when such values are encountered.

Steps to reproduce

from deepecho import PARModel
from deepecho.demo import load_demo

# Load demo data
data = load_demo()

# Define data types for all the columns
data_types = {
    'region': 'categorical',
    'day_of_week': 'categorical',
    'total_sales': 'continuous',
    'nb_customers': 'count',
}

model = PARModel(cuda=False)

# Learn a model from the data
model.fit(
    data=data,
    entity_columns=['store_id'],
    context_columns=['region'],
    data_types=data_types,
    sequence_index='date'
)

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-2-ca86fcdf00ef> in <module>
     21     context_columns=['region'],
     22     data_types=data_types,
---> 23     sequence_index='date'
     24 )
     25 

~/opt/anaconda3/envs/echo2/lib/python3.7/site-packages/deepecho/models/base.py in fit(self, data, entity_columns, context_columns, data_types, segment_size, sequence_index)
    166         # Validate and fit
    167         self._validate(sequences, context_types, data_types)
--> 168         self.fit_sequences(sequences, context_types, data_types)
    169 
    170         # Store context values

~/opt/anaconda3/envs/echo2/lib/python3.7/site-packages/deepecho/models/par.py in fit_sequences(self, sequences, context_types, data_types)
    330 
    331             optimizer.zero_grad()
--> 332             loss = self._compute_loss(X_padded[1:, :, :], Y_padded[:-1, :, :], seq_len)
    333             loss.backward()
    334             if self.verbose:

~/opt/anaconda3/envs/echo2/lib/python3.7/site-packages/deepecho/models/par.py in _compute_loss(self, X_padded, Y_padded, seq_len)
    387                     dist = torch.distributions.negative_binomial.NegativeBinomial(
    388                         r[:seq_len[i], i], p[:seq_len[i], i])
--> 389                     log_likelihood += torch.sum(dist.log_prob(x[:seq_len[i], i]))
    390 
    391                     p_true = X_padded[:seq_len[i], i, missing_idx]

~/opt/anaconda3/envs/echo2/lib/python3.7/site-packages/torch/distributions/negative_binomial.py in log_prob(self, value)
     90     def log_prob(self, value):
     91         if self._validate_args:
---> 92             self._validate_sample(value)
     93 
     94         log_unnormalized_prob = (self.total_count * F.logsigmoid(-self.logits) +

~/opt/anaconda3/envs/echo2/lib/python3.7/site-packages/torch/distributions/distribution.py in _validate_sample(self, value)
    275         assert support is not None
    276         if not support.check(value).all():
--> 277             raise ValueError('The value argument must be within the support')
    278 
    279     def _get_checked_instance(self, cls, _instance=None):

ValueError: The value argument must be within the support