def test_nan(): x = np.array([[1, np.nan, 3, 4], [5, 6, 7, np.nan], [9, 10, 11, 12]]) d = da.from_array(x, chunks=(2, 2)) assert_eq(np.nansum(x), da.nansum(d)) assert_eq(np.nansum(x, axis=0), da.nansum(d, axis=0)) assert_eq(np.nanmean(x, axis=1), da.nanmean(d, axis=1)) assert_eq(np.nanmin(x, axis=1), da.nanmin(d, axis=1)) assert_eq(np.nanmax(x, axis=(0, 1)), da.nanmax(d, axis=(0, 1))) assert_eq(np.nanvar(x), da.nanvar(d)) assert_eq(np.nanstd(x, axis=0), da.nanstd(d, axis=0)) assert_eq(np.nanargmin(x, axis=0), da.nanargmin(d, axis=0)) assert_eq(np.nanargmax(x, axis=0), da.nanargmax(d, axis=0)) assert_eq(np.nanprod(x), da.nanprod(d))
def test_nan(): x = np.array([[1, np.nan, 3, 4], [5, 6, 7, np.nan], [9, 10, 11, 12]]) d = da.from_array(x, blockshape=(2, 2)) assert eq(np.nansum(x), da.nansum(d)) assert eq(np.nansum(x, axis=0), da.nansum(d, axis=0)) assert eq(np.nanmean(x, axis=1), da.nanmean(d, axis=1)) assert eq(np.nanmin(x, axis=1), da.nanmin(d, axis=1)) assert eq(np.nanmax(x, axis=(0, 1)), da.nanmax(d, axis=(0, 1))) assert eq(np.nanvar(x), da.nanvar(d)) assert eq(np.nanstd(x, axis=0), da.nanstd(d, axis=0)) assert eq(np.nanargmin(x, axis=0), da.nanargmin(d, axis=0)) assert eq(np.nanargmax(x, axis=0), da.nanargmax(d, axis=0)) with ignoring(AttributeError): assert eq(np.nanprod(x), da.nanprod(d))
def test_nan(): x = np.array([[1, np.nan, 3, 4], [5, 6, 7, np.nan], [9, 10, 11, 12]]) d = da.from_array(x, chunks=(2, 2)) assert_eq(np.nansum(x), da.nansum(d)) assert_eq(np.nansum(x, axis=0), da.nansum(d, axis=0)) assert_eq(np.nanmean(x, axis=1), da.nanmean(d, axis=1)) assert_eq(np.nanmin(x, axis=1), da.nanmin(d, axis=1)) assert_eq(np.nanmax(x, axis=(0, 1)), da.nanmax(d, axis=(0, 1))) assert_eq(np.nanvar(x), da.nanvar(d)) assert_eq(np.nanstd(x, axis=0), da.nanstd(d, axis=0)) assert_eq(np.nanargmin(x, axis=0), da.nanargmin(d, axis=0)) assert_eq(np.nanargmax(x, axis=0), da.nanargmax(d, axis=0)) assert_eq(nanprod(x), da.nanprod(d))
def test_reductions(): x = np.arange(5).astype('f4') a = da.from_array(x, chunks=(2,)) assert eq(da.all(a), np.all(x)) assert eq(da.any(a), np.any(x)) assert eq(da.argmax(a, axis=0), np.argmax(x, axis=0)) assert eq(da.argmin(a, axis=0), np.argmin(x, axis=0)) assert eq(da.max(a), np.max(x)) assert eq(da.mean(a), np.mean(x)) assert eq(da.min(a), np.min(x)) assert eq(da.nanargmax(a, axis=0), np.nanargmax(x, axis=0)) assert eq(da.nanargmin(a, axis=0), np.nanargmin(x, axis=0)) assert eq(da.nanmax(a), np.nanmax(x)) assert eq(da.nanmin(a), np.nanmin(x)) assert eq(da.nansum(a), np.nansum(x)) assert eq(da.nanvar(a), np.nanvar(x)) assert eq(da.nanstd(a), np.nanstd(x))
def test_reductions(): x = np.arange(5).astype('f4') a = da.from_array(x, blockshape=(2, )) assert eq(da.all(a), np.all(x)) assert eq(da.any(a), np.any(x)) assert eq(da.argmax(a, axis=0), np.argmax(x, axis=0)) assert eq(da.argmin(a, axis=0), np.argmin(x, axis=0)) assert eq(da.max(a), np.max(x)) assert eq(da.mean(a), np.mean(x)) assert eq(da.min(a), np.min(x)) assert eq(da.nanargmax(a, axis=0), np.nanargmax(x, axis=0)) assert eq(da.nanargmin(a, axis=0), np.nanargmin(x, axis=0)) assert eq(da.nanmax(a), np.nanmax(x)) assert eq(da.nanmin(a), np.nanmin(x)) assert eq(da.nansum(a), np.nansum(x)) assert eq(da.nanvar(a), np.nanvar(x)) assert eq(da.nanstd(a), np.nanstd(x))
def fit(self, X, y=None): self._reset() attributes = OrderedDict() if isinstance(X, (pd.DataFrame, dd.DataFrame)): X = X.values if self.with_mean: mean_ = nanmean(X, 0) attributes["mean_"] = mean_ if self.with_std: var_ = nanvar(X, 0) scale_ = var_.copy() scale_[scale_ == 0] = 1 scale_ = da.sqrt(scale_) attributes["scale_"] = scale_ attributes["var_"] = var_ attributes["n_samples_seen_"] = np.nan values = compute(*attributes.values()) for k, v in zip(attributes, values): setattr(self, k, v) return self
def fit( self, X: Union[ArrayLike, DataFrameType], y: Optional[Union[ArrayLike, SeriesType]] = None, ) -> "StandardScaler": self._reset() X = self._validate_data( X, estimator=self, accept_dask_array=True, accept_dask_dataframe=True, accept_unknown_chunks=True, preserve_pandas_dataframe=True, ) attributes = OrderedDict() if isinstance(X, (pd.DataFrame, dd.DataFrame)): X = X.values if self.with_mean: mean_ = nanmean(X, 0) attributes["mean_"] = mean_ if self.with_std: var_ = nanvar(X, 0) scale_ = var_.copy() scale_[scale_ == 0] = 1 scale_ = da.sqrt(scale_) attributes["scale_"] = scale_ attributes["var_"] = var_ attributes["n_samples_seen_"] = X.shape[0] values = compute(*attributes.values()) for k, v in zip(attributes, values): setattr(self, k, v) self.n_features_in_: int = X.shape[1] return self