machine-learning.ipynb does not work

[anderl80]

Executing line

km = dask_ml.cluster.KMeans(n_clusters=3, init_max_iter=2, oversampling_factor=10)
km.fit(X)

results in

---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
<ipython-input-14-7d3bc4c475f7> in <module>()
      1 km = dask_ml.cluster.KMeans(n_clusters=3, init_max_iter=2, oversampling_factor=10)
----> 2 km.fit(X)

/opt/conda/lib/python3.6/site-packages/dask_ml/cluster/k_means.py in fit(self, X, y)
    197             max_iter=self.max_iter,
    198             init_max_iter=self.init_max_iter,
--> 199             tol=self.tol,
    200         )
    201         self.cluster_centers_ = centroids

/opt/conda/lib/python3.6/site-packages/dask_ml/cluster/k_means.py in k_means(X, n_clusters, init, precompute_distances, n_init, max_iter, verbose, tol, random_state, copy_x, n_jobs, algorithm, return_n_iter, oversampling_factor, init_max_iter)
    268         random_state=random_state,
    269         oversampling_factor=oversampling_factor,
--> 270         init_max_iter=init_max_iter,
    271     )
    272     if return_n_iter:

/opt/conda/lib/python3.6/site-packages/dask_ml/cluster/k_means.py in _kmeans_single_lloyd(X, n_clusters, max_iter, init, verbose, x_squared_norms, random_state, tol, precompute_distances, oversampling_factor, init_max_iter)
    550             counts = da.maximum(counts, 1)
    551             new_centers = new_centers / counts[:, None]
--> 552             new_centers, = compute(new_centers)
    553 
    554             # Convergence check

/opt/conda/lib/python3.6/site-packages/dask/base.py in compute(*args, **kwargs)
    400     keys = [x.__dask_keys__() for x in collections]
    401     postcomputes = [x.__dask_postcompute__() for x in collections]
--> 402     results = schedule(dsk, keys, **kwargs)
    403     return repack([f(r, *a) for r, (f, a) in zip(results, postcomputes)])
    404 

/opt/conda/lib/python3.6/site-packages/distributed/client.py in get(self, dsk, keys, restrictions, loose_restrictions, resources, sync, asynchronous, direct, retries, priority, fifo_timeout, **kwargs)
   2191             try:
   2192                 results = self.gather(packed, asynchronous=asynchronous,
-> 2193                                       direct=direct)
   2194             finally:
   2195                 for f in futures.values():

/opt/conda/lib/python3.6/site-packages/distributed/client.py in gather(self, futures, errors, maxsize, direct, asynchronous)
   1566             return self.sync(self._gather, futures, errors=errors,
   1567                              direct=direct, local_worker=local_worker,
-> 1568                              asynchronous=asynchronous)
   1569 
   1570     @gen.coroutine

/opt/conda/lib/python3.6/site-packages/distributed/client.py in sync(self, func, *args, **kwargs)
    651             return future
    652         else:
--> 653             return sync(self.loop, func, *args, **kwargs)
    654 
    655     def __repr__(self):

/opt/conda/lib/python3.6/site-packages/distributed/utils.py in sync(loop, func, *args, **kwargs)
    275             e.wait(10)
    276     if error[0]:
--> 277         six.reraise(*error[0])
    278     else:
    279         return result[0]

/opt/conda/lib/python3.6/site-packages/six.py in reraise(tp, value, tb)
    691             if value.__traceback__ is not tb:
    692                 raise value.with_traceback(tb)
--> 693             raise value
    694         finally:
    695             value = None

/opt/conda/lib/python3.6/site-packages/distributed/utils.py in f()
    260             if timeout is not None:
    261                 future = gen.with_timeout(timedelta(seconds=timeout), future)
--> 262             result[0] = yield future
    263         except Exception as exc:
    264             error[0] = sys.exc_info()

/opt/conda/lib/python3.6/site-packages/tornado/gen.py in run(self)
   1097 
   1098                     try:
-> 1099                         value = future.result()
   1100                     except Exception:
   1101                         self.had_exception = True

/opt/conda/lib/python3.6/site-packages/tornado/gen.py in run(self)
   1105                     if exc_info is not None:
   1106                         try:
-> 1107                             yielded = self.gen.throw(*exc_info)
   1108                         finally:
   1109                             # Break up a reference to itself

/opt/conda/lib/python3.6/site-packages/distributed/client.py in _gather(self, futures, errors, direct, local_worker)
   1445                             six.reraise(type(exception),
   1446                                         exception,
-> 1447                                         traceback)
   1448                     if errors == 'skip':
   1449                         bad_keys.add(key)

/opt/conda/lib/python3.6/site-packages/six.py in reraise(tp, value, tb)
    690                 value = tp()
    691             if value.__traceback__ is not tb:
--> 692                 raise value.with_traceback(tb)
    693             raise value
    694         finally:

/opt/conda/lib/python3.6/site-packages/numba/dispatcher.py in _compile_for_args()
    366                     e.patch_message(''.join(e.args) + help_msg)
    367             # ignore the FULL_TRACEBACKS config, this needs reporting!
--> 368             raise e
    369 
    370     def inspect_llvm(self, signature=None):

/opt/conda/lib/python3.6/site-packages/numba/dispatcher.py in _compile_for_args()
    323                 argtypes.append(self.typeof_pyval(a))
    324         try:
--> 325             return self.compile(tuple(argtypes))
    326         except errors.TypingError as e:
    327             # Intercept typing error that may be due to an argument

/opt/conda/lib/python3.6/site-packages/numba/dispatcher.py in compile()
    651 
    652                 self._cache_misses[sig] += 1
--> 653                 cres = self._compiler.compile(args, return_type)
    654                 self.add_overload(cres)
    655                 self._cache.save_overload(sig, cres)

/opt/conda/lib/python3.6/site-packages/numba/dispatcher.py in compile()
     81                                       args=args, return_type=return_type,
     82                                       flags=flags, locals=self.locals,
---> 83                                       pipeline_class=self.pipeline_class)
     84         # Check typing error if object mode is used
     85         if cres.typing_error is not None and not flags.enable_pyobject:

/opt/conda/lib/python3.6/site-packages/numba/compiler.py in compile_extra()
    871     pipeline = pipeline_class(typingctx, targetctx, library,
    872                               args, return_type, flags, locals)
--> 873     return pipeline.compile_extra(func)
    874 
    875 

/opt/conda/lib/python3.6/site-packages/numba/compiler.py in compile_extra()
    365         self.lifted = ()
    366         self.lifted_from = None
--> 367         return self._compile_bytecode()
    368 
    369     def compile_ir(self, func_ir, lifted=(), lifted_from=None):

/opt/conda/lib/python3.6/site-packages/numba/compiler.py in _compile_bytecode()
    802         """
    803         assert self.func_ir is None
--> 804         return self._compile_core()
    805 
    806     def _compile_ir(self):

/opt/conda/lib/python3.6/site-packages/numba/compiler.py in _compile_core()
    789         self.define_pipelines(pm)
    790         pm.finalize()
--> 791         res = pm.run(self.status)
    792         if res is not None:
    793             # Early pipeline completion

/opt/conda/lib/python3.6/site-packages/numba/compiler.py in run()
    251                     # No more fallback pipelines?
    252                     if is_final_pipeline:
--> 253                         raise patched_exception
    254                     # Go to next fallback pipeline
    255                     else:

/opt/conda/lib/python3.6/site-packages/numba/compiler.py in run()
    243                 try:
    244                     event(stage_name)
--> 245                     stage()
    246                 except _EarlyPipelineCompletion as e:
    247                     return e.result

/opt/conda/lib/python3.6/site-packages/numba/compiler.py in stage_generic_rewrites()
    478         with self.fallback_context(msg):
    479             rewrites.rewrite_registry.apply('before-inference',
--> 480                                             self, self.func_ir)
    481 
    482     def stage_nopython_rewrites(self):

/opt/conda/lib/python3.6/site-packages/numba/rewrites/registry.py in apply()
     68                 key, block = work_list.pop()
     69                 matches = rewrite.match(func_ir, block, pipeline.typemap,
---> 70                                         pipeline.calltypes)
     71                 if matches:
     72                     if config.DEBUG or config.DUMP_IR:

/opt/conda/lib/python3.6/site-packages/numba/rewrites/static_getitem.py in match()
     19             if expr.op == 'getitem':
     20                 try:
---> 21                     const = func_ir.infer_constant(expr.index)
     22                 except errors.ConstantInferenceError:
     23                     continue

/opt/conda/lib/python3.6/site-packages/numba/ir.py in infer_constant()
    962         if isinstance(name, Var):
    963             name = name.name
--> 964         return self._consts.infer_constant(name)
    965 
    966     def get_definition(self, value, lhs_only=False):

/opt/conda/lib/python3.6/site-packages/numba/consts.py in infer_constant()
     32         if name not in self._cache:
     33             try:
---> 34                 self._cache[name] = (True, self._do_infer(name))
     35             except ConstantInferenceError as exc:
     36                 # Store the exception args only, to avoid keeping

/opt/conda/lib/python3.6/site-packages/numba/consts.py in _do_infer()
     58                 "no single definition for %r" % (name,))
     59         try:
---> 60             const = defn.infer_constant()
     61         except ConstantInferenceError:
     62             if isinstance(defn, ir.Expr):

/opt/conda/lib/python3.6/site-packages/numba/ir.py in infer_constant()
    344 
    345     def infer_constant(self):
--> 346         raise ConstantInferenceError('%s' % self, loc=self.loc)
    347 
    348 

/opt/conda/lib/python3.6/site-packages/numba/errors.py in __init__()
    526         self.value = value
    527         msg = "Cannot make a constant from: %s" % value
--> 528         super(ConstantInferenceError, self).__init__(msg, loc=loc)
    529 
    530 

/opt/conda/lib/python3.6/site-packages/numba/errors.py in __init__()
    386         if loc:
    387             super(NumbaError, self).__init__(
--> 388                 highlight("%s\n%s\n" % (msg, loc.strformat())))
    389         else:
    390             super(NumbaError, self).__init__(highlight("%s" % (msg,)))

/opt/conda/lib/python3.6/site-packages/numba/ir.py in strformat()
     85 
     86             ret.extend(selected[:-1])
---> 87             ret.append(_termcolor.highlight(selected[-1]))
     88 
     89             # point at the problem with a caret

IndexError: Failed at nopython (nopython rewrites)
list index out of range

[robmarkcole]

Also the imports raise the error:

ImportError                               Traceback (most recent call last)
<ipython-input-3-4af4d06194bc> in <module>()
----> 1 import dask_ml.joblib  # register the distriubted backend
      2 from sklearn.datasets import make_classification
      3 from sklearn.svm import SVC
      4 from sklearn.model_selection import GridSearchCV
      5 import pandas as pd

/srv/conda/lib/python3.6/site-packages/dask_ml/joblib.py in <module>()
----> 1 import distributed.joblib  # noqa

/srv/conda/lib/python3.6/site-packages/distributed/joblib.py in <module>()
     17 
     18 
---> 19 raise ImportError(msg)

ImportError:  It is no longer necessary to `import dask_ml.joblib` or
`import distributed.joblib`.

This functionality has moved into the core Joblib codebase.

To use Joblib's Dask backend with Scikit-Learn >= 0.20.0

    from dask.distributed import Client
    client = Client()

    from sklearn.externals import joblib

    with joblib.parallel_backend('dask'):
        # your scikit-learn code

See http:https://ml.dask.org/joblib.html for more information.

[robmarkcole]

@anderl80 I am able to execute the entire notebook. Try putting import dask_ml.joblib in its own cell

[mrocklin]

You no longer need the joblib import (joblib/sklearn upstreamed the dask functionality). Probably though we should just remove the notebook from this repository. The Pangeo community shouldn't have to deal with this maintenance burden.

…

On Fri, Dec 21, 2018 at 3:45 AM Robin ***@***.***> wrote: @anderl80 <https://github.com/anderl80> I am able to execute the entire notebook — You are receiving this because you are subscribed to this thread. Reply to this email directly, view it on GitHub <#27 (comment)>, or mute the thread <https://github.com/notifications/unsubscribe-auth/AASszAb0Ts6d4Ptd1yH0UIsbcW0W1D8gks5u7Mn2gaJpZM4Yn5Y8> .