Ejemplo n.º 1
0
 // Close all AppendableVec
 public Futures closeAppendables(Futures fs) {
   _col0 = null; // Reset cache
   int len = vecs().length;
   for (int i = 0; i < len; i++) {
     Vec v = _vecs[i];
     if (v instanceof AppendableVec)
       DKV.put(_keys[i], _vecs[i] = ((AppendableVec) v).close(fs), fs);
   }
   return fs;
 }
Ejemplo n.º 2
0
 public Vec replace(int col, Vec nv) {
   assert col < _names.length;
   Vec rv = vecs()[col];
   assert rv.group().equals(nv.group());
   _vecs[col] = nv;
   _keys[col] = nv._key;
   if (DKV.get(nv._key) == null) // If not already in KV, put it there
   DKV.put(nv._key, nv);
   return rv;
 }
Ejemplo n.º 3
0
 public Frame(String[] names, Vec[] vecs) {
   // assert names==null || names.length == vecs.length : "Number of columns does not match to
   // number of cols' names.";
   _names = names;
   _vecs = vecs;
   _keys = new Key[vecs.length];
   for (int i = 0; i < vecs.length; i++) {
     Key k = _keys[i] = vecs[i]._key;
     if (DKV.get(k) == null) // If not already in KV, put it there
     DKV.put(k, vecs[i]);
   }
   Vec v0 = anyVec();
   if (v0 == null) return;
   VectorGroup grp = v0.group();
   for (int i = 0; i < vecs.length; i++) assert grp.equals(vecs[i].group());
 }
Ejemplo n.º 4
0
 @Override
 public void map(Chunk cs) {
   int idx = _chunkOffset + cs.cidx();
   Key ckey = Vec.chunkKey(_v._key, idx);
   if (_cmap != null) {
     assert !cs.hasFloat()
         : "Input chunk (" + cs.getClass() + ") has float, but is expected to be categorical";
     NewChunk nc = new NewChunk(_v, idx);
     // loop over rows and update ints for new domain mapping according to vecs[c].domain()
     for (int r = 0; r < cs._len; ++r) {
       if (cs.isNA(r)) nc.addNA();
       else nc.addNum(_cmap[(int) cs.at8(r)], 0);
     }
     nc.close(_fs);
   } else {
     DKV.put(ckey, cs.deepCopy(), _fs, true);
   }
 }
Ejemplo n.º 5
0
    /**
     * Train a Deep Learning model, assumes that all members are populated If checkpoint == null,
     * then start training a new model, otherwise continue from a checkpoint
     */
    public final void buildModel() {
      DeepLearningModel cp = null;
      if (_parms._checkpoint == null) {
        cp =
            new DeepLearningModel(
                dest(),
                _parms,
                new DeepLearningModel.DeepLearningModelOutput(DeepLearning.this),
                _train,
                _valid,
                nclasses());
        cp.model_info().initializeMembers();
      } else {
        final DeepLearningModel previous = DKV.getGet(_parms._checkpoint);
        if (previous == null) throw new IllegalArgumentException("Checkpoint not found.");
        Log.info("Resuming from checkpoint.");
        _job.update(0, "Resuming from checkpoint");

        if (isClassifier() != previous._output.isClassifier())
          throw new H2OIllegalArgumentException(
              "Response type must be the same as for the checkpointed model.");
        if (isSupervised() != previous._output.isSupervised())
          throw new H2OIllegalArgumentException(
              "Model type must be the same as for the checkpointed model.");

        // check the user-given arguments for consistency
        DeepLearningParameters oldP =
            previous._parms; // sanitized parameters for checkpointed model
        DeepLearningParameters newP = _parms; // user-given parameters for restart

        DeepLearningParameters oldP2 = (DeepLearningParameters) oldP.clone();
        DeepLearningParameters newP2 = (DeepLearningParameters) newP.clone();
        DeepLearningParameters.Sanity.modifyParms(
            oldP, oldP2, nclasses()); // sanitize the user-given parameters
        DeepLearningParameters.Sanity.modifyParms(
            newP, newP2, nclasses()); // sanitize the user-given parameters
        DeepLearningParameters.Sanity.checkpoint(oldP2, newP2);

        DataInfo dinfo;
        try {
          // PUBDEV-2513: Adapt _train and _valid (in-place) to match the frames that were used for
          // the previous model
          // This can add or remove dummy columns (can happen if the dataset is sparse and datasets
          // have different non-const columns)
          for (String st : previous.adaptTestForTrain(_train, true, false)) Log.warn(st);
          for (String st : previous.adaptTestForTrain(_valid, true, false)) Log.warn(st);
          dinfo = makeDataInfo(_train, _valid, _parms, nclasses());
          DKV.put(dinfo);
          cp = new DeepLearningModel(dest(), _parms, previous, false, dinfo);
          cp.write_lock(_job);

          if (!Arrays.equals(cp._output._names, previous._output._names)) {
            throw new H2OIllegalArgumentException(
                "The columns of the training data must be the same as for the checkpointed model. Check ignored columns (or disable ignore_const_cols).");
          }
          if (!Arrays.deepEquals(cp._output._domains, previous._output._domains)) {
            throw new H2OIllegalArgumentException(
                "Categorical factor levels of the training data must be the same as for the checkpointed model.");
          }
          if (dinfo.fullN() != previous.model_info().data_info().fullN()) {
            throw new H2OIllegalArgumentException(
                "Total number of predictors is different than for the checkpointed model.");
          }
          if (_parms._epochs <= previous.epoch_counter) {
            throw new H2OIllegalArgumentException(
                "Total number of epochs must be larger than the number of epochs already trained for the checkpointed model ("
                    + previous.epoch_counter
                    + ").");
          }

          // these are the mutable parameters that are to be used by the model (stored in
          // model_info._parms)
          final DeepLearningParameters actualNewP =
              cp.model_info()
                  .get_params(); // actually used parameters for model building (defaults filled in,
                                 // etc.)
          assert (actualNewP != previous.model_info().get_params());
          assert (actualNewP != newP);
          assert (actualNewP != oldP);
          DeepLearningParameters.Sanity.update(actualNewP, newP, nclasses());

          Log.info(
              "Continuing training after "
                  + String.format("%.3f", previous.epoch_counter)
                  + " epochs from the checkpointed model.");
          cp.update(_job);
        } catch (H2OIllegalArgumentException ex) {
          if (cp != null) {
            cp.unlock(_job);
            cp.delete();
            cp = null;
          }
          throw ex;
        } finally {
          if (cp != null) cp.unlock(_job);
        }
      }
      trainModel(cp);

      // clean up, but don't delete weights and biases if user asked for export
      List<Key> keep = new ArrayList<>();
      try {
        if (_parms._export_weights_and_biases
            && cp._output.weights != null
            && cp._output.biases != null) {
          for (Key k : Arrays.asList(cp._output.weights)) {
            keep.add(k);
            for (Vec vk : ((Frame) DKV.getGet(k)).vecs()) {
              keep.add(vk._key);
            }
          }
          for (Key k : Arrays.asList(cp._output.biases)) {
            keep.add(k);
            for (Vec vk : ((Frame) DKV.getGet(k)).vecs()) {
              keep.add(vk._key);
            }
          }
        }
      } finally {
        Scope.exit(keep.toArray(new Key[keep.size()]));
      }
    }
Ejemplo n.º 6
0
 @Override
 public void onCompletion(CountedCompleter cc) {
   DKV.put(_v);
 }