![]() DirectQuery achieves parity with in-memory models through support for a wide array of data sources, ability to handle calculated tables and columns in a DirectQuery model, row level security via DAX expressions that reach the back-end database, and query optimizations that result in faster throughput. While in-memory models are the default, DirectQuery is an alternative query mode for models that are either too large to fit in memory, or when data volatility precludes a reasonable processing strategy. ![]() By using state-of-the-art compression algorithms and multi-threaded query processor, the Analysis Services VertiPaq analytics engine delivers fast access to tabular model objects and data by reporting client applications like Power BI and Excel. float()) def decodes( self, o): to = TabularPandas(o, _names, _names, _names) to = (to) return TabularLine(pd.Series()) class ReadTabTarget(ItemTransform): def _init_( self, proc): self.proc = proc def encodes( self, row): return row.astype(np.int64) def decodes( self, o): return Category( models in Analysis Services are databases that run in-memory or in DirectQuery mode, connecting to data from back-end relational data sources. _getitem_) for o in ( _names, _names)) return TensorTabular(tensor(cats). Other target types Multi-label categories one-hot encoded labelĬlass TensorTabular(fastuple): def get_ctxs( self, max_n = 10, **kwargs): n_samples = min( self.shape, max_n) df = pd.DataFrame(index = range(n_samples)) return for i in range(n_samples)] def display( self, ctxs): display_df(pd.DataFrame(ctxs)) class TabularLine(pd.Series): "A line of a dataframe that knows how to show itself" def show( self, ctx = None, **kwargs): return self if ctx is None else ctx.append( self) class ReadTabLine(ItemTransform): def _init_( self, proc): self.proc = proc def encodes( self, row): cats,conts = (o. We can decode any set of transformed data by calling to.decode_row with our raw data: Sample=None, shuffle_fn=None, do_batch=None)Ī transformed DataLoader for Tabular data Integration exampleįor a more in-depth explanation, see the tabular tutorial Persistent_workers=False, pin_memory_device='', wif=None,īefore_iter=None, after_item=None, before_batch=None,Īfter_iter=None, create_batches=None, create_item=None,Ĭreate_batch=None, retain=None, get_idxs=None, Pin_memory=False, timeout=0, batch_size=None,ĭrop_last=False, indexed=None, n=None, device=None, Num_workers=0, verbose:bool=False, do_setup:bool=True, TabDataLoader TabDataLoader (dataset, bs=16, shuffle=False, after_batch=None, Transform TabularPandas values into a Tensor with the ability to decode Namespace containing the various filling strategies.Ĭurrently, filling with the median, a constant, and the mode are supported.įillMissing FillMissing (fill_strategy=, add_col=True,įill the missing values in continuous columns. While visually in the DataFrame you will not see a change, the classes are stored in to.procs.categorify as we can see below on a dummy DataFrame: Transform the categorical variables to something similar to pd.Categorical These transforms are applied as soon as the data is available rather than as data is called from the DataLoaderĬategorify Categorify (enc=None, dec=None, split_idx=None, order=None) TabularProc TabularProc (enc=None, dec=None, split_idx=None, order=None)īase class to write a non-lazy tabular processor for dataframes Y_names=None, y_block=None, splits=None, do_setup=True,ĭevice=None, inplace=False, reduce_memory=True) TabularPandas TabularPandas (df, procs=None, cat_names=None, cont_names=None, reduce_memory: fastai will attempt to reduce the overall memory usage by the inputted DataFrame with df_shrink.You should ensure pd._assignment is None before setting this inplace: If True, Tabular will not keep a separate copy of your original DataFrame in memory.do_setup: A parameter for if Tabular will run the data through the procs upon initialization.y_block: How to sub-categorize the type of y_names ( CategoryBlock or RegressionBlock).Note: Mixed y’s such as Regression and Classification is not currently supported, however multiple regression or classification outputs is.cont_names: Your continuous x variables.cat_names: Your categorical x variables.Y_block=None, splits=None, do_setup=True, device=None,Ī DataFrame wrapper that knows which cols are cont/cat/y, and returns rows in _getitem_ Tabular Tabular (df, procs=None, cat_names=None, cont_names=None, y_names=None, We reduced the overall memory used by 79%!
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