Top 10 Examples of "dask in functional component" in Python

def test_pca_inverse():
    # Test that the projection of data can be inverted
    rng = np.random.RandomState(0)
    n, p = 50, 3
    X = rng.randn(n, p)  # spherical data
    X[:, 1] *= 0.00001  # make middle component relatively small
    X += [5, 4, 3]  # make a large mean
    dX = da.from_array(X, chunks=(n // 2, p))

    # same check that we can find the original data from the transformed
    # signal (since the data is almost of rank n_components)
    pca = dd.PCA(n_components=2, svd_solver="full").fit(dX)
    Y = pca.transform(dX)
    Y_inverse = pca.inverse_transform(Y)
    assert_almost_equal(X, Y_inverse, decimal=3)

    # same as above with whitening (approximate reconstruction)
    for solver in solver_list:
        pca = dd.PCA(n_components=2, whiten=True, svd_solver=solver)
        pca.fit(dX)
        Y = pca.transform(dX)
        Y_inverse = pca.inverse_transform(Y)
        assert_eq(dX, Y_inverse, atol=1e-3)

dfParts = []
            chunkSize = min(self.CHUNK_SIZE, self.nEvents / self.ncores)
            nPartitions = int(self.nEvents // chunkSize) + 1
            # Determine the column names
            gNames = kwds.pop('groupnames', self.getGroupNames(wexpr='Stream'))
            colNames = self.name2alias(gNames)

            for p in range(nPartitions): # Generate partitioned dataframe

                # Calculate the starting and ending index of every chunk of events
                eventIDStart = int(p * chunkSize)
                eventIDEnd = int(min(eventIDStart + chunkSize, self.nEvents))
                dfParts.append(d.delayed(self._assembleGroups)(gNames, amin=eventIDStart, amax=eventIDEnd, **kwds))

            # Construct eda (event dask array) and edf (event dask dataframe)
            eda = da.from_array(np.concatenate(d.compute(*dfParts), axis=1).T, chunks=self.CHUNK_SIZE)
            self.edf = ddf.from_dask_array(eda, columns=colNames)

            if ret == True:
                return self.edf

        # Delayed array for loading an HDF5 file of reasonable size (e.g. < 1GB)
        elif form == 'darray':

            gNames = kwds.pop('groupnames', self.getGroupNames(wexpr='Stream'))
            darray = d.delayed(self._assembleGroups)(gNames, amin=None, amax=None, timeStamps=timeStamps, ret='array', **kwds)


            if ret == True:
                return darray

def _check_inputs(self, X, accept_sparse_negative=False, copy=False):
        kwargs = {}
        if SK_022:
            kwargs["copy"] = copy

        if isinstance(X, (pd.DataFrame, dd.DataFrame)):
            X = X.values
        if isinstance(X, np.ndarray):
            C = len(X) // min(multiprocessing.cpu_count(), 2)
            X = da.from_array(X, chunks=C)

        rng = check_random_state(self.random_state)
        # TODO: non-float dtypes?
        # TODO: sparse arrays?
        # TODO: mix of sparse, dense?
        sample = rng.uniform(size=(5, X.shape[1])).astype(X.dtype)
        super(QuantileTransformer, self)._check_inputs(
            sample, accept_sparse_negative=accept_sparse_negative, **kwargs
        )
        return X

Optimizes chunk size in different orientations to facilitate rapid
            screening of algorithm output
        
        Returns
        -------
        darray : Dask Array
        chunk_init : tuple (len 3), chunk size before ghosting.  Used in select cases
        """
    
        # Compute chunk size and convert if not a Dask Array
        if not isinstance(darray, da.core.Array):  
            chunk_size = util.compute_chunk_size(darray.shape, 
                                               darray.dtype.itemsize, 
                                               kernel=kernel,
                                               preview=preview)
            darray = da.from_array(darray, chunks=chunk_size)
            chunks_init = darray.chunks            
                
        else:
            chunks_init = darray.chunks
        
        # Ghost Dask Array if operation specifies a kernel
        if kernel != None:
                hw = tuple(np.array(kernel) // 2)
                darray = da.ghost.ghost(darray, depth=hw, boundary='reflect')
                
        return(darray, chunks_init)

def test_incremental_basic(scheduler, dataframes):
    # Create observations that we know linear models can recover
    n, d = 100, 3
    rng = da.random.RandomState(42)
    X = rng.normal(size=(n, d), chunks=30)
    coef_star = rng.uniform(size=d, chunks=d)
    y = da.sign(X.dot(coef_star))
    y = (y + 1) / 2
    if dataframes:
        X = dd.from_array(X)
        y = dd.from_array(y)

    with scheduler() as (s, [_, _]):
        est1 = SGDClassifier(random_state=0, tol=1e-3, average=True)
        est2 = clone(est1)

        clf = Incremental(est1, random_state=0)
        result = clf.fit(X, y, classes=[0, 1])
        assert result is clf

def test_basic(self, output_distribution):
        rs = da.random.RandomState(0)
        a = dpp.QuantileTransformer(output_distribution=output_distribution)
        b = spp.QuantileTransformer(output_distribution=output_distribution)

        X = rs.uniform(size=(1000, 3), chunks=50)
        a.fit(X)
        b.fit(X)
        assert_estimator_equal(a, b, atol=0.02)

        # set the quantiles, so that from here out, we're exact
        a.quantiles_ = b.quantiles_
        assert_eq_ar(a.transform(X), b.transform(X), atol=1e-7)
        assert_eq_ar(X, a.inverse_transform(a.transform(X)))

def test_fit_shuffle_blocks():
    N = 10
    X = da.from_array(1 + np.arange(N).reshape(-1, 1), chunks=1)
    y = da.from_array(np.ones(N), chunks=1)
    classes = [0, 1]

    sgd = SGDClassifier(
        max_iter=5, random_state=0, fit_intercept=False, shuffle=False, tol=1e-3
    )

    sgd1 = fit(clone(sgd), X, y, random_state=0, classes=classes)
    sgd2 = fit(clone(sgd), X, y, random_state=42, classes=classes)
    assert len(sgd1.coef_) == len(sgd2.coef_) == 1
    assert not np.allclose(sgd1.coef_, sgd2.coef_)

    X, y = make_classification(random_state=0, chunks=20)
    sgd_a = fit(clone(sgd), X, y, random_state=0, classes=classes, shuffle_blocks=False)
    sgd_b = fit(
        clone(sgd), X, y, random_state=42, classes=classes, shuffle_blocks=False

def huge_2d_array():
    array = np.vstack(1000 * [np.arange(0, 1000)])
    return da.from_array(array, chunks=(500, 500))

def test_dask_dataframe(self):
        with LocalCUDACluster() as cluster:
            with Client(cluster) as client:
                X, y = generate_array()

                X = dd.from_dask_array(X)
                y = dd.from_dask_array(y)

                X = X.map_partitions(cudf.from_pandas)
                y = y.map_partitions(cudf.from_pandas)

                dtrain = dxgb.DaskDMatrix(client, X, y)
                out = dxgb.train(client, {'tree_method': 'gpu_hist'},
                                 dtrain=dtrain,
                                 evals=[(dtrain, 'X')],
                                 num_boost_round=2)

                assert isinstance(out['booster'], dxgb.Booster)
                assert len(out['history']['X']['rmse']) == 2

                predictions = dxgb.predict(client, out, dtrain).compute()
                assert isinstance(predictions, np.ndarray)

def test_example(query, expected, model):
    if model == 'dask':
        sc = {k: dd.from_pandas(df, npartitions=3) for k, df in scope.items()}
        actual = fq.execute(query, scope=sc, model=model)
        actual = actual.compute()

    else:
        actual = fq.execute(query, scope=scope, model=model)

    expected = expected()

    # set empty columns in expected to the ones in actual
    expected.columns = [e or a for a, e in zip(actual.columns, expected.columns)]

    actual = actual.reset_index(drop=True)
    expected = actual.reset_index(drop=True)

    pdt.assert_frame_equal(actual, expected, check_dtype=False)