Numpy is not available error

[WenjieDu]

Discussed in #31

^{Originally posted by lauredecaudin January 25, 2023}
Hello !

I tried to use the package with the code found in the README,

import numpy as np
from sklearn.preprocessing import StandardScaler
from pypots.data import load_specific_dataset, mcar, masked_fill
from pypots.imputation import SAITS
from pypots.utils.metrics import cal_mae
# Data preprocessing. Tedious, but PyPOTS can help. 🤓
data = load_specific_dataset('physionet_2012')  # PyPOTS will automatically download and extract it.
X = data['X']
num_samples = len(X['RecordID'].unique())
X = X.drop('RecordID', axis = 1)
X = StandardScaler().fit_transform(X.to_numpy())
X = X.reshape(num_samples, 48, -1)
X_intact, X, missing_mask, indicating_mask = mcar(X, 0.1) # hold out 10% observed values as ground truth
X = masked_fill(X, 1 - missing_mask, np.nan)
# Model training. This is PyPOTS showtime. 💪
saits = SAITS(n_steps=48, n_features=37, n_layers=2, d_model=256, d_inner=128, n_head=4, d_k=64, d_v=64, dropout=0.1, epochs=10)
saits.fit(X)  # train the model. Here I use the whole dataset as the training set, because ground truth is not visible to the model.
imputation = saits.impute(X)  # impute the originally-missing values and artificially-missing values
mae = cal_mae(imputation, X_intact, indicating_mask)

And I get this error :

---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
<command-282728034361361> in <module>
     15 # Model training. This is PyPOTS showtime. 💪
     16 saits = SAITS(n_steps=48, n_features=37, n_layers=2, d_model=256, d_inner=128, n_head=4, d_k=64, d_v=64, dropout=0.1, epochs=10)
---> 17 saits.fit(X)  # train the model. Here I use the whole dataset as the training set, because ground truth is not visible to the model.
     18 imputation = saits.impute(X)  # impute the originally-missing values and artificially-missing values
     19 mae = cal_mae(imputation, X_intact, indicating_mask)

/databricks/python/lib/python3.8/site-packages/pypots/imputation/saits.py in fit(self, train_X, val_X)
    216 
    217     def fit(self, train_X, val_X=None):
--> 218         train_X = self.check_input(self.n_steps, self.n_features, train_X)
    219         if val_X is not None:
    220             val_X = self.check_input(self.n_steps, self.n_features, val_X)

/databricks/python/lib/python3.8/site-packages/pypots/base.py in check_input(self, expected_n_steps, expected_n_features, X, y, out_dtype)
     76                 X = torch.tensor(X).to(self.device)
     77             elif is_array:
---> 78                 X = torch.from_numpy(X).to(self.device)
     79             else:  # is tensor
     80                 X = X.to(self.device)

RuntimeError: Numpy is not available

Anyone knows what's happening ?

I use Databricks with a cluster 10.4 LTS https://docs.databricks.com/release-notes/runtime/10.4.html
I installed PyPots with pip install pypots

Thanks

[WenjieDu]

Solved. Please check out the solution here.