microsoftml.rx_neural_network: rete neurale

Utilizzo

microsoftml.rx_neural_network(formula: str,
    data: [revoscalepy.datasource.RxDataSource.RxDataSource,
    pandas.core.frame.DataFrame], method: ['binary', 'multiClass',
    'regression'] = 'binary', num_hidden_nodes: int = 100,
    num_iterations: int = 100,
    optimizer: [<function adadelta_optimizer at 0x0000007156EAC048>,
    <function sgd_optimizer at 0x0000007156E9FB70>] = {'Name': 'SgdOptimizer',
    'Settings': {}}, net_definition: str = None,
    init_wts_diameter: float = 0.1, max_norm: float = 0,
    acceleration: [<function avx_math at 0x0000007156E9FEA0>,
    <function clr_math at 0x0000007156EAC158>,
    <function gpu_math at 0x0000007156EAC1E0>,
    <function mkl_math at 0x0000007156EAC268>,
    <function sse_math at 0x0000007156EAC2F0>] = {'Name': 'AvxMath',
    'Settings': {}}, mini_batch_size: int = 1, normalize: ['No',
    'Warn', 'Auto', 'Yes'] = 'Auto', ml_transforms: list = None,
    ml_transform_vars: list = None, row_selection: str = None,
    transforms: dict = None, transform_objects: dict = None,
    transform_function: str = None,
    transform_variables: list = None,
    transform_packages: list = None,
    transform_environment: dict = None, blocks_per_read: int = None,
    report_progress: int = None, verbose: int = 1,
    ensemble: microsoftml.modules.ensemble.EnsembleControl = None,
    compute_context: revoscalepy.computecontext.RxComputeContext.RxComputeContext = None)

Descrizione

Reti neurali per la modellazione di regressione e per la classificazione binaria e multiclasse.

Dettagli

Una rete neurale è una classe di modelli di previsione ispirati al cervello umano. Una rete neurale può essere rappresentata come un grafico diretto ponderato. Ogni nodo nel grafico è chiamato neurone. I neuroni nel grafico sono disposti in livelli, dove i neuroni di un livello sono collegati da un margine ponderato (i pesi possono essere 0 o numeri positivi) ai neuroni del livello successivo. Il primo livello è chiamato livello di input e ogni neurone nel livello di input corrisponde a una delle caratteristiche. L'ultimo livello della funzione è chiamato livello di output. Quindi nel caso delle reti neurali binarie contiene due neuroni di output, uno per ogni classe, i cui valori sono le probabilità di appartenere a ciascuna classe. I livelli rimanenti vengono definiti livelli nascosti. I valori dei neuroni nei livelli nascosti e nel livello di output vengono impostati calcolando la somma ponderata dei valori dei neuroni nel livello precedente e applicando una funzione di attivazione a quella somma ponderata. Un modello di rete neurale è definito dalla struttura del suo grafico (vale a dire, il numero di livelli nascosti e il numero di neuroni in ogni livello nascosto), dalla scelta della funzione di attivazione e dai pesi sui margini del grafico. L'algoritmo della rete neurale cerca di apprendere i pesi ottimali sui margini in base ai dati di training.

Anche se le reti neurali siano ampiamente conosciute per l'uso nel Deep Learning e nella modellazione di problemi complessi come il riconoscimento delle immagini, sono anche facilmente adattabili ai problemi di regressione. Qualsiasi classe di modelli statistici può essere considerata una rete neurale se usa pesi adattivi e può approssimare funzioni non lineari dei relativi input. La regressione della rete neurale è particolarmente adatta a problemi in cui un modello di regressione più tradizionale non può adattarsi a una soluzione.

Argomenti

formula

Formula come descritto in revoscalepy.rx_formula. I termini di interazione e F() non sono attualmente supportati in microsoftml.

data

Oggetto origine dati o stringa di caratteri che specifica un file con estensione xdf o un oggetto frame di dati.

method

Una stringa di caratteri che indica il tipo Fast Tree:

• "binary" per la rete neurale di classificazione binaria predefinita.

• "multiClass" per la rete neurale di classificazione multiclasse.

• "regression" per una rete neurale di regressione.

num_hidden_nodes

Numero predefinito di nodi nascosti nella rete neurale. Il valore predefinito è 100.

num_iterations

Numero di iterazioni nel set di training completo. Il valore predefinito è 100.

optimizer

Elenco che specifica l'algoritmo di ottimizzazione sgd o adaptive. Questo elenco può essere creato usando sgd_optimizer o adadelta_optimizer. Il valore predefinito è sgd.

net_definition

Definizione Net# della struttura della rete neurale. Per ulteriori informazioni sul linguaggio Net#, vedere Guida di riferimento

init_wts_diameter

Imposta il diametro dei pesi iniziali che specifica l'intervallo da cui vengono estratti i valori per i pesi di apprendimento iniziali. I pesi vengono inizializzati in modo casuale all'interno di questo intervallo. Il valore predefinito è 0,1.

max_norm

Specifica un limite superiore per vincolare la norma del vettore di peso in ingresso a ciascuna unità nascosta. Questo può essere molto importante nelle reti neurali max out e nei casi in cui il training produca pesi illimitati.

accelerazione

Specifica il tipo di accelerazione hardware da usare. I valori possibili sono "sse_math" e "gpu_math". Per l'accelerazione GPU, si consiglia di usare un miniBatchSize maggiore di uno. Se si intende usare l'accelerazione GPU, sono necessari ulteriori passaggi di configurazione manuale:

• Scaricare e installare NVidia CUDA Toolkit 6.5 (CUDA Toolkit).

• Scaricare e installare NVIDIA cuDNN v2 Library (cudnn Library).

• Trovare la directory libs del pacchetto microsoftml chiamando import microsoftml, os, os.path.join(microsoftml.__path__[0], "mxLibs").

• Copiare cublas64_65.dll, cudart64_65.dll e cusparse64_65.dll da CUDA Toolkit 6.5 nella directory libs del pacchetto microsoftml.

• Copiare cudnn64_65.dll dalla libreria cuDNN v2 nella directory libs del pacchetto microsoftml.

mini_batch_size

Imposta la dimensione del mini-batch. I valori consigliati sono compresi tra 1 e 256. Questo parametro viene utilizzato solo quando l'accelerazione è GPU. L'impostazione di questo parametro su un valore più alto migliora la velocità del training, ma potrebbe influire negativamente sulla precisione. Il valore predefinito è 1.

normalize

Specifica il tipo di normalizzazione automatica usata:

• "Warn": se la normalizzazione è necessaria, viene eseguita automaticamente. Questa è l'opzione predefinita.

• "No": non viene eseguita alcuna normalizzazione.

• "Yes": la normalizzazione viene eseguita.

• "Auto": se la normalizzazione è necessaria, viene visualizzato un avviso ma la normalizzazione non viene eseguita.

La normalizzazione ridimensiona diversi intervalli di dati in base a una scala standard. Il ridimensionamento delle funzioni assicura che le distanze tra i punti dati siano proporzionali e consente di accelerare significativamente la convergenza di diversi metodi di ottimizzazione, tra cui la discesa di gradiente. Se la normalizzazione viene eseguita, viene usato un normalizzatore MaxMin. I valori vengono normalizzati in un intervallo [a, b], dove -1 <= a <= 0 e 0 <= b <= 1 e b - a = 1. Questo normalizzatore mantiene la sparsità eseguendo il mapping di zero a zero.

ml_transforms

Specifica un elenco di trasformazioni di MicrosoftML da eseguire sui dati prima del training o None se non devono essere eseguite trasformazioni. Per informazioni sulle trasformazioni supportate, vedere featurize_text, categorical e categorical_hash. Queste trasformazioni vengono eseguite dopo eventuali trasformazioni Python specificate. Il valore predefinito è None.

ml_transform_vars

Specifica un vettore di caratteri di nomi di variabili da usare in ml_transforms o None se non è necessario usarne alcuno. Il valore predefinito è None.

row_selection

NON SUPPORTATO. Specifica le righe (osservazioni) dal set di dati che devono essere usate dal modello con il nome di una variabile logica dal set di dati (tra virgolette) o con un'espressione logica tramite variabili nel set di dati. Ad esempio:

• row_selection = "old" userà solo osservazioni in cui il valore della variabile old è True.

• row_selection = (age > 20) & (age < 65) & (log(income) > 10) usa solo osservazioni in cui il valore della variabile age è compreso tra 20 e 65 e il valore di log della variabile income è maggiore di 10.

La selezione delle righe viene eseguita dopo l'elaborazione di eventuali trasformazioni dei dati. Vedere gli argomenti transforms o transform_function. Analogamente a tutte le espressioni, è possibile definire row_selection all'esterno della chiamata alla funzione usando la funzione expression.

trasformazioni

NON SUPPORTATO. Espressione con un formato che rappresenta il primo ciclo di trasformazioni delle variabili. Analogamente a tutte le espressioni, è possibile definire transforms o row_selection all'esterno della chiamata alla funzione usando la funzione expression.

transform_objects

NON SUPPORTATO. Elenco denominato che contiene oggetti a cui transforms, transform_functione row_selection possono fare riferimento.

transform_function

Funzione di trasformazione della variabile.

transform_variables

Vettore di caratteri delle variabili del set di dati di input necessario per la funzione di trasformazione.

transform_packages

NON SUPPORTATO. Vettore di caratteri che specifica altri pacchetti Python, oltre a quelli specificati in RxOptions.get_option("transform_packages"), da rendere disponibili e precaricati per l'uso nelle funzioni di trasformazione delle variabili. Ad esempio, quelli definiti in modo esplicito nelle funzioni revoscalepy tramite i relativi argomenti transforms e transform_function o quelli definiti in modo implicito tramite i relativi argomenti formula o row_selection. L'argomento transform_packages può anche essere NoneRxOptions.get_option("transform_packages"), che indica che non vengono precaricati pacchetti esterni a .

transform_environment

NON SUPPORTATO. Ambiente definito dall'utente da usare come elemento padre di tutti gli ambienti sviluppati internamente e usati per la trasformazione dei dati delle variabili. Se transform_environment = None, un nuovo ambiente "hash" con revoscalepy.baseenvis padre usato.

blocks_per_read

Specifica il numero di blocchi da leggere per ogni blocco di dati letto dall'origine dati.

report_progress

Valore intero che specifica il livello di creazione di report sullo stato di elaborazione delle righe:

• 0: non viene segnalato alcun avanzamento.

• 1: il numero di righe elaborate viene stampato e aggiornato.

• 2: vengono segnalate le righe elaborate e le tempistiche.

• 3: vengono segnalate le righe elaborate e tutte le tempistiche.

verbose

Valore intero che specifica la quantità di output desiderata. Se 0, non viene stampato alcun output dettagliato durante i calcoli. Valori interi da 1 a 4 per fornire quantità crescenti di informazioni.

compute_context

Imposta il contesto in cui vengono eseguiti i calcoli, specificato con un revoscalepy.RxComputeContext valido. Sono attualmente supportati contesti di calcolo locali e revoscalepy.RxInSqlServer.

ensemble

Parametri di controllo per l'ensembling.

Restituisce

Oggetto NeuralNetwork con il modello sottoposto a training.

Nota

Questo algoritmo è a thread singolo e non tenterà di caricare l'intero set di dati in memoria.

Vedi anche

adadelta_optimizer, sgd_optimizer, avx_math, clr_math, gpu_math, mkl_math, sse_math, rx_predict.

Riferimenti

Wikipedia: Rete neurale artificiale

Esempio di classificazione binaria

'''
Binary Classification.
'''
import numpy
import pandas
from microsoftml import rx_neural_network, rx_predict
from revoscalepy.etl.RxDataStep import rx_data_step
from microsoftml.datasets.datasets import get_dataset

infert = get_dataset("infert")

import sklearn
if sklearn.__version__ < "0.18":
    from sklearn.cross_validation import train_test_split
else:
    from sklearn.model_selection import train_test_split

infertdf = infert.as_df()
infertdf["isCase"] = infertdf.case == 1
data_train, data_test, y_train, y_test = train_test_split(infertdf, infertdf.isCase)

forest_model = rx_neural_network(
    formula=" isCase ~ age + parity + education + spontaneous + induced ",
    data=data_train)
    
# RuntimeError: The type (RxTextData) for file is not supported.
score_ds = rx_predict(forest_model, data=data_test,
                     extra_vars_to_write=["isCase", "Score"])
                     
# Print the first five rows
print(rx_data_step(score_ds, number_rows_read=5))

Output:

Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.
Beginning processing data.
Rows Read: 186, Read Time: 0, Transform Time: 0
Beginning processing data.
Beginning processing data.
Rows Read: 186, Read Time: 0, Transform Time: 0
Beginning processing data.
Beginning processing data.
Rows Read: 186, Read Time: 0, Transform Time: 0
Beginning processing data.
Using: AVX Math

***** Net definition *****
  input Data [5];
  hidden H [100] sigmoid { // Depth 1
    from Data all;
  }
  output Result [1] sigmoid { // Depth 0
    from H all;
  }
***** End net definition *****
Input count: 5
Output count: 1
Output Function: Sigmoid
Loss Function: LogLoss
PreTrainer: NoPreTrainer
___________________________________________________________________
Starting training...
Learning rate: 0.001000
Momentum: 0.000000
InitWtsDiameter: 0.100000
___________________________________________________________________
Initializing 1 Hidden Layers, 701 Weights...
Estimated Pre-training MeanError = 0.742343
Iter:1/100, MeanErr=0.680245(-8.37%), 119.87M WeightUpdates/sec
Iter:2/100, MeanErr=0.637843(-6.23%), 122.52M WeightUpdates/sec
Iter:3/100, MeanErr=0.635404(-0.38%), 122.24M WeightUpdates/sec
Iter:4/100, MeanErr=0.634980(-0.07%), 73.36M WeightUpdates/sec
Iter:5/100, MeanErr=0.635287(0.05%), 128.26M WeightUpdates/sec
Iter:6/100, MeanErr=0.634572(-0.11%), 131.05M WeightUpdates/sec
Iter:7/100, MeanErr=0.634827(0.04%), 124.27M WeightUpdates/sec
Iter:8/100, MeanErr=0.635359(0.08%), 123.69M WeightUpdates/sec
Iter:9/100, MeanErr=0.635244(-0.02%), 119.35M WeightUpdates/sec
Iter:10/100, MeanErr=0.634712(-0.08%), 127.80M WeightUpdates/sec
Iter:11/100, MeanErr=0.635105(0.06%), 122.69M WeightUpdates/sec
Iter:12/100, MeanErr=0.635226(0.02%), 98.61M WeightUpdates/sec
Iter:13/100, MeanErr=0.634977(-0.04%), 127.88M WeightUpdates/sec
Iter:14/100, MeanErr=0.634347(-0.10%), 123.25M WeightUpdates/sec
Iter:15/100, MeanErr=0.634891(0.09%), 124.27M WeightUpdates/sec
Iter:16/100, MeanErr=0.635116(0.04%), 123.06M WeightUpdates/sec
Iter:17/100, MeanErr=0.633770(-0.21%), 122.05M WeightUpdates/sec
Iter:18/100, MeanErr=0.634992(0.19%), 128.79M WeightUpdates/sec
Iter:19/100, MeanErr=0.634385(-0.10%), 122.95M WeightUpdates/sec
Iter:20/100, MeanErr=0.634752(0.06%), 127.14M WeightUpdates/sec
Iter:21/100, MeanErr=0.635043(0.05%), 123.44M WeightUpdates/sec
Iter:22/100, MeanErr=0.634845(-0.03%), 121.81M WeightUpdates/sec
Iter:23/100, MeanErr=0.634850(0.00%), 125.11M WeightUpdates/sec
Iter:24/100, MeanErr=0.634617(-0.04%), 122.18M WeightUpdates/sec
Iter:25/100, MeanErr=0.634675(0.01%), 125.69M WeightUpdates/sec
Iter:26/100, MeanErr=0.634911(0.04%), 122.44M WeightUpdates/sec
Iter:27/100, MeanErr=0.634311(-0.09%), 121.90M WeightUpdates/sec
Iter:28/100, MeanErr=0.634798(0.08%), 123.54M WeightUpdates/sec
Iter:29/100, MeanErr=0.634674(-0.02%), 127.53M WeightUpdates/sec
Iter:30/100, MeanErr=0.634546(-0.02%), 100.96M WeightUpdates/sec
Iter:31/100, MeanErr=0.634859(0.05%), 124.40M WeightUpdates/sec
Iter:32/100, MeanErr=0.634747(-0.02%), 128.21M WeightUpdates/sec
Iter:33/100, MeanErr=0.634842(0.02%), 125.82M WeightUpdates/sec
Iter:34/100, MeanErr=0.634703(-0.02%), 77.48M WeightUpdates/sec
Iter:35/100, MeanErr=0.634804(0.02%), 122.21M WeightUpdates/sec
Iter:36/100, MeanErr=0.634690(-0.02%), 112.48M WeightUpdates/sec
Iter:37/100, MeanErr=0.634654(-0.01%), 119.18M WeightUpdates/sec
Iter:38/100, MeanErr=0.634885(0.04%), 137.19M WeightUpdates/sec
Iter:39/100, MeanErr=0.634723(-0.03%), 113.80M WeightUpdates/sec
Iter:40/100, MeanErr=0.634714(0.00%), 127.50M WeightUpdates/sec
Iter:41/100, MeanErr=0.634794(0.01%), 129.54M WeightUpdates/sec
Iter:42/100, MeanErr=0.633835(-0.15%), 133.05M WeightUpdates/sec
Iter:43/100, MeanErr=0.634401(0.09%), 128.95M WeightUpdates/sec
Iter:44/100, MeanErr=0.634575(0.03%), 123.42M WeightUpdates/sec
Iter:45/100, MeanErr=0.634673(0.02%), 123.78M WeightUpdates/sec
Iter:46/100, MeanErr=0.634692(0.00%), 119.04M WeightUpdates/sec
Iter:47/100, MeanErr=0.634476(-0.03%), 122.95M WeightUpdates/sec
Iter:48/100, MeanErr=0.634583(0.02%), 97.87M WeightUpdates/sec
Iter:49/100, MeanErr=0.634706(0.02%), 121.41M WeightUpdates/sec
Iter:50/100, MeanErr=0.634564(-0.02%), 120.58M WeightUpdates/sec
Iter:51/100, MeanErr=0.634118(-0.07%), 120.17M WeightUpdates/sec
Iter:52/100, MeanErr=0.634699(0.09%), 127.27M WeightUpdates/sec
Iter:53/100, MeanErr=0.634123(-0.09%), 110.51M WeightUpdates/sec
Iter:54/100, MeanErr=0.634390(0.04%), 123.74M WeightUpdates/sec
Iter:55/100, MeanErr=0.634461(0.01%), 113.66M WeightUpdates/sec
Iter:56/100, MeanErr=0.634415(-0.01%), 118.61M WeightUpdates/sec
Iter:57/100, MeanErr=0.634453(0.01%), 114.99M WeightUpdates/sec
Iter:58/100, MeanErr=0.634478(0.00%), 104.53M WeightUpdates/sec
Iter:59/100, MeanErr=0.634010(-0.07%), 124.62M WeightUpdates/sec
Iter:60/100, MeanErr=0.633901(-0.02%), 118.93M WeightUpdates/sec
Iter:61/100, MeanErr=0.634088(0.03%), 40.46M WeightUpdates/sec
Iter:62/100, MeanErr=0.634046(-0.01%), 94.65M WeightUpdates/sec
Iter:63/100, MeanErr=0.634233(0.03%), 27.18M WeightUpdates/sec
Iter:64/100, MeanErr=0.634596(0.06%), 123.94M WeightUpdates/sec
Iter:65/100, MeanErr=0.634185(-0.06%), 125.01M WeightUpdates/sec
Iter:66/100, MeanErr=0.634469(0.04%), 119.41M WeightUpdates/sec
Iter:67/100, MeanErr=0.634333(-0.02%), 124.11M WeightUpdates/sec
Iter:68/100, MeanErr=0.634203(-0.02%), 112.68M WeightUpdates/sec
Iter:69/100, MeanErr=0.633854(-0.05%), 118.62M WeightUpdates/sec
Iter:70/100, MeanErr=0.634319(0.07%), 123.59M WeightUpdates/sec
Iter:71/100, MeanErr=0.634423(0.02%), 122.51M WeightUpdates/sec
Iter:72/100, MeanErr=0.634388(-0.01%), 126.15M WeightUpdates/sec
Iter:73/100, MeanErr=0.634230(-0.02%), 126.51M WeightUpdates/sec
Iter:74/100, MeanErr=0.634011(-0.03%), 128.32M WeightUpdates/sec
Iter:75/100, MeanErr=0.634294(0.04%), 127.48M WeightUpdates/sec
Iter:76/100, MeanErr=0.634372(0.01%), 123.51M WeightUpdates/sec
Iter:77/100, MeanErr=0.632020(-0.37%), 122.12M WeightUpdates/sec
Iter:78/100, MeanErr=0.633770(0.28%), 119.55M WeightUpdates/sec
Iter:79/100, MeanErr=0.633504(-0.04%), 124.21M WeightUpdates/sec
Iter:80/100, MeanErr=0.634154(0.10%), 125.94M WeightUpdates/sec
Iter:81/100, MeanErr=0.633491(-0.10%), 120.83M WeightUpdates/sec
Iter:82/100, MeanErr=0.634212(0.11%), 128.60M WeightUpdates/sec
Iter:83/100, MeanErr=0.634138(-0.01%), 73.58M WeightUpdates/sec
Iter:84/100, MeanErr=0.634244(0.02%), 124.08M WeightUpdates/sec
Iter:85/100, MeanErr=0.634065(-0.03%), 96.43M WeightUpdates/sec
Iter:86/100, MeanErr=0.634174(0.02%), 124.28M WeightUpdates/sec
Iter:87/100, MeanErr=0.633966(-0.03%), 125.24M WeightUpdates/sec
Iter:88/100, MeanErr=0.633989(0.00%), 130.31M WeightUpdates/sec
Iter:89/100, MeanErr=0.633767(-0.04%), 115.73M WeightUpdates/sec
Iter:90/100, MeanErr=0.633831(0.01%), 122.81M WeightUpdates/sec
Iter:91/100, MeanErr=0.633219(-0.10%), 114.91M WeightUpdates/sec
Iter:92/100, MeanErr=0.633589(0.06%), 93.29M WeightUpdates/sec
Iter:93/100, MeanErr=0.634086(0.08%), 123.31M WeightUpdates/sec
Iter:94/100, MeanErr=0.634075(0.00%), 120.99M WeightUpdates/sec
Iter:95/100, MeanErr=0.634071(0.00%), 122.49M WeightUpdates/sec
Iter:96/100, MeanErr=0.633523(-0.09%), 116.48M WeightUpdates/sec
Iter:97/100, MeanErr=0.634103(0.09%), 128.85M WeightUpdates/sec
Iter:98/100, MeanErr=0.633836(-0.04%), 123.87M WeightUpdates/sec
Iter:99/100, MeanErr=0.633772(-0.01%), 128.17M WeightUpdates/sec
Iter:100/100, MeanErr=0.633684(-0.01%), 123.65M WeightUpdates/sec
Done!
Estimated Post-training MeanError = 0.631268
___________________________________________________________________
Not training a calibrator because it is not needed.
Elapsed time: 00:00:00.2454094
Elapsed time: 00:00:00.0082325
Beginning processing data.
Rows Read: 62, Read Time: 0.001, Transform Time: 0
Beginning processing data.
Elapsed time: 00:00:00.0297006
Finished writing 62 rows.
Writing completed.
Rows Read: 5, Total Rows Processed: 5, Total Chunk Time: 0.001 seconds 
  isCase PredictedLabel     Score  Probability
0   True          False -0.689636     0.334114
1   True          False -0.710219     0.329551
2   True          False -0.712912     0.328956
3  False          False -0.700765     0.331643
4   True          False -0.689783     0.334081

Esempio di classificazione MultiClass

'''
MultiClass Classification.
'''
import numpy
import pandas
from microsoftml import rx_neural_network, rx_predict
from revoscalepy.etl.RxDataStep import rx_data_step
from microsoftml.datasets.datasets import get_dataset

iris = get_dataset("iris")

import sklearn
if sklearn.__version__ < "0.18":
    from sklearn.cross_validation import train_test_split
else:
    from sklearn.model_selection import train_test_split

irisdf = iris.as_df()
irisdf["Species"] = irisdf["Species"].astype("category")
data_train, data_test, y_train, y_test = train_test_split(irisdf, irisdf.Species)

model = rx_neural_network(
    formula="  Species ~ Sepal_Length + Sepal_Width + Petal_Length + Petal_Width ",
    method="multiClass",
    data=data_train)
    
# RuntimeError: The type (RxTextData) for file is not supported.
score_ds = rx_predict(model, data=data_test,
                     extra_vars_to_write=["Species", "Score"])
                     
# Print the first five rows
print(rx_data_step(score_ds, number_rows_read=5))

Output:

Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.
Beginning processing data.
Rows Read: 112, Read Time: 0.001, Transform Time: 0
Beginning processing data.
Beginning processing data.
Rows Read: 112, Read Time: 0, Transform Time: 0
Beginning processing data.
Beginning processing data.
Rows Read: 112, Read Time: 0, Transform Time: 0
Beginning processing data.
Using: AVX Math

***** Net definition *****
  input Data [4];
  hidden H [100] sigmoid { // Depth 1
    from Data all;
  }
  output Result [3] softmax { // Depth 0
    from H all;
  }
***** End net definition *****
Input count: 4
Output count: 3
Output Function: SoftMax
Loss Function: LogLoss
PreTrainer: NoPreTrainer
___________________________________________________________________
Starting training...
Learning rate: 0.001000
Momentum: 0.000000
InitWtsDiameter: 0.100000
___________________________________________________________________
Initializing 1 Hidden Layers, 803 Weights...
Estimated Pre-training MeanError = 1.949606
Iter:1/100, MeanErr=1.937924(-0.60%), 98.43M WeightUpdates/sec
Iter:2/100, MeanErr=1.921153(-0.87%), 96.21M WeightUpdates/sec
Iter:3/100, MeanErr=1.920000(-0.06%), 95.55M WeightUpdates/sec
Iter:4/100, MeanErr=1.917267(-0.14%), 81.25M WeightUpdates/sec
Iter:5/100, MeanErr=1.917611(0.02%), 102.44M WeightUpdates/sec
Iter:6/100, MeanErr=1.918476(0.05%), 106.16M WeightUpdates/sec
Iter:7/100, MeanErr=1.916096(-0.12%), 97.85M WeightUpdates/sec
Iter:8/100, MeanErr=1.919486(0.18%), 77.99M WeightUpdates/sec
Iter:9/100, MeanErr=1.916452(-0.16%), 95.67M WeightUpdates/sec
Iter:10/100, MeanErr=1.916024(-0.02%), 102.06M WeightUpdates/sec
Iter:11/100, MeanErr=1.917155(0.06%), 99.21M WeightUpdates/sec
Iter:12/100, MeanErr=1.918543(0.07%), 99.25M WeightUpdates/sec
Iter:13/100, MeanErr=1.919120(0.03%), 85.38M WeightUpdates/sec
Iter:14/100, MeanErr=1.917713(-0.07%), 103.00M WeightUpdates/sec
Iter:15/100, MeanErr=1.917675(0.00%), 98.70M WeightUpdates/sec
Iter:16/100, MeanErr=1.917982(0.02%), 99.10M WeightUpdates/sec
Iter:17/100, MeanErr=1.916254(-0.09%), 103.41M WeightUpdates/sec
Iter:18/100, MeanErr=1.915691(-0.03%), 102.00M WeightUpdates/sec
Iter:19/100, MeanErr=1.914844(-0.04%), 86.64M WeightUpdates/sec
Iter:20/100, MeanErr=1.919268(0.23%), 94.68M WeightUpdates/sec
Iter:21/100, MeanErr=1.918748(-0.03%), 108.11M WeightUpdates/sec
Iter:22/100, MeanErr=1.917997(-0.04%), 96.33M WeightUpdates/sec
Iter:23/100, MeanErr=1.914987(-0.16%), 82.84M WeightUpdates/sec
Iter:24/100, MeanErr=1.916550(0.08%), 99.70M WeightUpdates/sec
Iter:25/100, MeanErr=1.915401(-0.06%), 96.69M WeightUpdates/sec
Iter:26/100, MeanErr=1.916092(0.04%), 101.62M WeightUpdates/sec
Iter:27/100, MeanErr=1.916381(0.02%), 98.81M WeightUpdates/sec
Iter:28/100, MeanErr=1.917414(0.05%), 102.29M WeightUpdates/sec
Iter:29/100, MeanErr=1.917316(-0.01%), 100.17M WeightUpdates/sec
Iter:30/100, MeanErr=1.916507(-0.04%), 82.09M WeightUpdates/sec
Iter:31/100, MeanErr=1.915786(-0.04%), 98.33M WeightUpdates/sec
Iter:32/100, MeanErr=1.917581(0.09%), 101.70M WeightUpdates/sec
Iter:33/100, MeanErr=1.913680(-0.20%), 79.94M WeightUpdates/sec
Iter:34/100, MeanErr=1.917264(0.19%), 102.54M WeightUpdates/sec
Iter:35/100, MeanErr=1.917377(0.01%), 100.67M WeightUpdates/sec
Iter:36/100, MeanErr=1.912060(-0.28%), 70.37M WeightUpdates/sec
Iter:37/100, MeanErr=1.917009(0.26%), 80.80M WeightUpdates/sec
Iter:38/100, MeanErr=1.916216(-0.04%), 94.56M WeightUpdates/sec
Iter:39/100, MeanErr=1.916362(0.01%), 28.22M WeightUpdates/sec
Iter:40/100, MeanErr=1.910658(-0.30%), 100.87M WeightUpdates/sec
Iter:41/100, MeanErr=1.916375(0.30%), 85.99M WeightUpdates/sec
Iter:42/100, MeanErr=1.916257(-0.01%), 102.06M WeightUpdates/sec
Iter:43/100, MeanErr=1.914505(-0.09%), 99.86M WeightUpdates/sec
Iter:44/100, MeanErr=1.914638(0.01%), 103.11M WeightUpdates/sec
Iter:45/100, MeanErr=1.915141(0.03%), 107.62M WeightUpdates/sec
Iter:46/100, MeanErr=1.915119(0.00%), 99.65M WeightUpdates/sec
Iter:47/100, MeanErr=1.915379(0.01%), 107.03M WeightUpdates/sec
Iter:48/100, MeanErr=1.912565(-0.15%), 104.78M WeightUpdates/sec
Iter:49/100, MeanErr=1.915466(0.15%), 110.43M WeightUpdates/sec
Iter:50/100, MeanErr=1.914038(-0.07%), 98.44M WeightUpdates/sec
Iter:51/100, MeanErr=1.915015(0.05%), 96.28M WeightUpdates/sec
Iter:52/100, MeanErr=1.913771(-0.06%), 89.27M WeightUpdates/sec
Iter:53/100, MeanErr=1.911621(-0.11%), 72.67M WeightUpdates/sec
Iter:54/100, MeanErr=1.914969(0.18%), 111.17M WeightUpdates/sec
Iter:55/100, MeanErr=1.913894(-0.06%), 98.68M WeightUpdates/sec
Iter:56/100, MeanErr=1.914871(0.05%), 95.41M WeightUpdates/sec
Iter:57/100, MeanErr=1.912898(-0.10%), 80.72M WeightUpdates/sec
Iter:58/100, MeanErr=1.913334(0.02%), 103.71M WeightUpdates/sec
Iter:59/100, MeanErr=1.913362(0.00%), 99.57M WeightUpdates/sec
Iter:60/100, MeanErr=1.913915(0.03%), 106.21M WeightUpdates/sec
Iter:61/100, MeanErr=1.913310(-0.03%), 112.27M WeightUpdates/sec
Iter:62/100, MeanErr=1.913395(0.00%), 50.86M WeightUpdates/sec
Iter:63/100, MeanErr=1.912814(-0.03%), 58.91M WeightUpdates/sec
Iter:64/100, MeanErr=1.911468(-0.07%), 72.06M WeightUpdates/sec
Iter:65/100, MeanErr=1.912313(0.04%), 86.34M WeightUpdates/sec
Iter:66/100, MeanErr=1.913320(0.05%), 114.39M WeightUpdates/sec
Iter:67/100, MeanErr=1.912914(-0.02%), 105.97M WeightUpdates/sec
Iter:68/100, MeanErr=1.909881(-0.16%), 105.73M WeightUpdates/sec
Iter:69/100, MeanErr=1.911649(0.09%), 105.23M WeightUpdates/sec
Iter:70/100, MeanErr=1.911192(-0.02%), 110.24M WeightUpdates/sec
Iter:71/100, MeanErr=1.912480(0.07%), 106.86M WeightUpdates/sec
Iter:72/100, MeanErr=1.909881(-0.14%), 97.28M WeightUpdates/sec
Iter:73/100, MeanErr=1.911678(0.09%), 109.57M WeightUpdates/sec
Iter:74/100, MeanErr=1.911137(-0.03%), 91.01M WeightUpdates/sec
Iter:75/100, MeanErr=1.910706(-0.02%), 99.41M WeightUpdates/sec
Iter:76/100, MeanErr=1.910869(0.01%), 84.18M WeightUpdates/sec
Iter:77/100, MeanErr=1.911643(0.04%), 105.07M WeightUpdates/sec
Iter:78/100, MeanErr=1.911438(-0.01%), 110.12M WeightUpdates/sec
Iter:79/100, MeanErr=1.909590(-0.10%), 84.16M WeightUpdates/sec
Iter:80/100, MeanErr=1.911181(0.08%), 92.30M WeightUpdates/sec
Iter:81/100, MeanErr=1.910534(-0.03%), 110.60M WeightUpdates/sec
Iter:82/100, MeanErr=1.909340(-0.06%), 54.07M WeightUpdates/sec
Iter:83/100, MeanErr=1.908275(-0.06%), 104.08M WeightUpdates/sec
Iter:84/100, MeanErr=1.910364(0.11%), 107.19M WeightUpdates/sec
Iter:85/100, MeanErr=1.910286(0.00%), 102.55M WeightUpdates/sec
Iter:86/100, MeanErr=1.909155(-0.06%), 79.72M WeightUpdates/sec
Iter:87/100, MeanErr=1.909384(0.01%), 102.37M WeightUpdates/sec
Iter:88/100, MeanErr=1.907751(-0.09%), 105.48M WeightUpdates/sec
Iter:89/100, MeanErr=1.910164(0.13%), 102.53M WeightUpdates/sec
Iter:90/100, MeanErr=1.907935(-0.12%), 105.03M WeightUpdates/sec
Iter:91/100, MeanErr=1.909510(0.08%), 99.97M WeightUpdates/sec
Iter:92/100, MeanErr=1.907405(-0.11%), 100.03M WeightUpdates/sec
Iter:93/100, MeanErr=1.905757(-0.09%), 113.21M WeightUpdates/sec
Iter:94/100, MeanErr=1.909167(0.18%), 107.86M WeightUpdates/sec
Iter:95/100, MeanErr=1.907593(-0.08%), 106.09M WeightUpdates/sec
Iter:96/100, MeanErr=1.908358(0.04%), 111.25M WeightUpdates/sec
Iter:97/100, MeanErr=1.906484(-0.10%), 95.81M WeightUpdates/sec
Iter:98/100, MeanErr=1.908239(0.09%), 105.89M WeightUpdates/sec
Iter:99/100, MeanErr=1.908508(0.01%), 103.05M WeightUpdates/sec
Iter:100/100, MeanErr=1.904747(-0.20%), 106.81M WeightUpdates/sec
Done!
Estimated Post-training MeanError = 1.896338
___________________________________________________________________
Not training a calibrator because it is not needed.
Elapsed time: 00:00:00.1620840
Elapsed time: 00:00:00.0096627
Beginning processing data.
Rows Read: 38, Read Time: 0, Transform Time: 0
Beginning processing data.
Elapsed time: 00:00:00.0312987
Finished writing 38 rows.
Writing completed.
Rows Read: 5, Total Rows Processed: 5, Total Chunk Time: Less than .001 seconds 
      Species   Score.0   Score.1   Score.2
0  versicolor  0.350161  0.339557  0.310282
1      setosa  0.358506  0.336593  0.304901
2   virginica  0.346957  0.340573  0.312470
3   virginica  0.346685  0.340748  0.312567
4   virginica  0.348469  0.340113  0.311417

Esempio di regressione

'''
Regression.
'''
import numpy
import pandas
from microsoftml import rx_neural_network, rx_predict
from revoscalepy.etl.RxDataStep import rx_data_step
from microsoftml.datasets.datasets import get_dataset

attitude = get_dataset("attitude")

import sklearn
if sklearn.__version__ < "0.18":
    from sklearn.cross_validation import train_test_split
else:
    from sklearn.model_selection import train_test_split

attitudedf = attitude.as_df()
data_train, data_test = train_test_split(attitudedf)

model = rx_neural_network(
    formula="rating ~ complaints + privileges + learning + raises + critical + advance",
    method="regression",
    data=data_train)
    
# RuntimeError: The type (RxTextData) for file is not supported.
score_ds = rx_predict(model, data=data_test,
                     extra_vars_to_write=["rating"])
                     
# Print the first five rows
print(rx_data_step(score_ds, number_rows_read=5))

Output:

Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.
Beginning processing data.
Rows Read: 22, Read Time: 0, Transform Time: 0
Beginning processing data.
Beginning processing data.
Rows Read: 22, Read Time: 0.001, Transform Time: 0
Beginning processing data.
Beginning processing data.
Rows Read: 22, Read Time: 0, Transform Time: 0
Beginning processing data.
Using: AVX Math

***** Net definition *****
  input Data [6];
  hidden H [100] sigmoid { // Depth 1
    from Data all;
  }
  output Result [1] linear { // Depth 0
    from H all;
  }
***** End net definition *****
Input count: 6
Output count: 1
Output Function: Linear
Loss Function: SquaredLoss
PreTrainer: NoPreTrainer
___________________________________________________________________
Starting training...
Learning rate: 0.001000
Momentum: 0.000000
InitWtsDiameter: 0.100000
___________________________________________________________________
Initializing 1 Hidden Layers, 801 Weights...
Estimated Pre-training MeanError = 4458.793673
Iter:1/100, MeanErr=1624.747024(-63.56%), 27.30M WeightUpdates/sec
Iter:2/100, MeanErr=139.267390(-91.43%), 30.50M WeightUpdates/sec
Iter:3/100, MeanErr=116.382316(-16.43%), 29.16M WeightUpdates/sec
Iter:4/100, MeanErr=114.947244(-1.23%), 32.06M WeightUpdates/sec
Iter:5/100, MeanErr=112.886818(-1.79%), 32.96M WeightUpdates/sec
Iter:6/100, MeanErr=112.406547(-0.43%), 30.29M WeightUpdates/sec
Iter:7/100, MeanErr=110.502757(-1.69%), 30.92M WeightUpdates/sec
Iter:8/100, MeanErr=111.499645(0.90%), 31.20M WeightUpdates/sec
Iter:9/100, MeanErr=111.895816(0.36%), 32.46M WeightUpdates/sec
Iter:10/100, MeanErr=110.171443(-1.54%), 34.61M WeightUpdates/sec
Iter:11/100, MeanErr=106.975524(-2.90%), 22.14M WeightUpdates/sec
Iter:12/100, MeanErr=107.708220(0.68%), 7.73M WeightUpdates/sec
Iter:13/100, MeanErr=105.345097(-2.19%), 28.99M WeightUpdates/sec
Iter:14/100, MeanErr=109.937833(4.36%), 31.04M WeightUpdates/sec
Iter:15/100, MeanErr=106.672340(-2.97%), 30.04M WeightUpdates/sec
Iter:16/100, MeanErr=108.474555(1.69%), 32.41M WeightUpdates/sec
Iter:17/100, MeanErr=109.449054(0.90%), 31.60M WeightUpdates/sec
Iter:18/100, MeanErr=105.911830(-3.23%), 34.05M WeightUpdates/sec
Iter:19/100, MeanErr=106.045172(0.13%), 33.80M WeightUpdates/sec
Iter:20/100, MeanErr=108.360427(2.18%), 33.60M WeightUpdates/sec
Iter:21/100, MeanErr=106.506436(-1.71%), 33.77M WeightUpdates/sec
Iter:22/100, MeanErr=99.167335(-6.89%), 32.26M WeightUpdates/sec
Iter:23/100, MeanErr=108.115797(9.02%), 25.86M WeightUpdates/sec
Iter:24/100, MeanErr=106.292283(-1.69%), 31.03M WeightUpdates/sec
Iter:25/100, MeanErr=99.397875(-6.49%), 31.33M WeightUpdates/sec
Iter:26/100, MeanErr=104.805299(5.44%), 31.57M WeightUpdates/sec
Iter:27/100, MeanErr=101.385085(-3.26%), 22.92M WeightUpdates/sec
Iter:28/100, MeanErr=100.064656(-1.30%), 35.01M WeightUpdates/sec
Iter:29/100, MeanErr=100.519013(0.45%), 32.74M WeightUpdates/sec
Iter:30/100, MeanErr=99.273143(-1.24%), 35.12M WeightUpdates/sec
Iter:31/100, MeanErr=100.465649(1.20%), 33.68M WeightUpdates/sec
Iter:32/100, MeanErr=102.402320(1.93%), 33.79M WeightUpdates/sec
Iter:33/100, MeanErr=97.517196(-4.77%), 32.32M WeightUpdates/sec
Iter:34/100, MeanErr=102.597511(5.21%), 32.46M WeightUpdates/sec
Iter:35/100, MeanErr=96.187788(-6.25%), 32.32M WeightUpdates/sec
Iter:36/100, MeanErr=101.533507(5.56%), 21.44M WeightUpdates/sec
Iter:37/100, MeanErr=99.339624(-2.16%), 21.53M WeightUpdates/sec
Iter:38/100, MeanErr=98.049306(-1.30%), 15.27M WeightUpdates/sec
Iter:39/100, MeanErr=97.508282(-0.55%), 23.21M WeightUpdates/sec
Iter:40/100, MeanErr=99.894288(2.45%), 27.94M WeightUpdates/sec
Iter:41/100, MeanErr=95.190566(-4.71%), 32.47M WeightUpdates/sec
Iter:42/100, MeanErr=91.234977(-4.16%), 31.29M WeightUpdates/sec
Iter:43/100, MeanErr=98.824414(8.32%), 32.35M WeightUpdates/sec
Iter:44/100, MeanErr=96.759533(-2.09%), 22.37M WeightUpdates/sec
Iter:45/100, MeanErr=95.275106(-1.53%), 32.09M WeightUpdates/sec
Iter:46/100, MeanErr=95.749031(0.50%), 26.49M WeightUpdates/sec
Iter:47/100, MeanErr=96.267879(0.54%), 31.81M WeightUpdates/sec
Iter:48/100, MeanErr=97.383752(1.16%), 31.01M WeightUpdates/sec
Iter:49/100, MeanErr=96.605199(-0.80%), 32.05M WeightUpdates/sec
Iter:50/100, MeanErr=96.927400(0.33%), 32.42M WeightUpdates/sec
Iter:51/100, MeanErr=96.288491(-0.66%), 28.89M WeightUpdates/sec
Iter:52/100, MeanErr=92.751171(-3.67%), 33.68M WeightUpdates/sec
Iter:53/100, MeanErr=88.655001(-4.42%), 34.53M WeightUpdates/sec
Iter:54/100, MeanErr=90.923513(2.56%), 32.00M WeightUpdates/sec
Iter:55/100, MeanErr=91.627261(0.77%), 25.74M WeightUpdates/sec
Iter:56/100, MeanErr=91.132907(-0.54%), 30.00M WeightUpdates/sec
Iter:57/100, MeanErr=95.294092(4.57%), 33.13M WeightUpdates/sec
Iter:58/100, MeanErr=90.219024(-5.33%), 31.70M WeightUpdates/sec
Iter:59/100, MeanErr=92.727605(2.78%), 30.71M WeightUpdates/sec
Iter:60/100, MeanErr=86.910488(-6.27%), 33.07M WeightUpdates/sec
Iter:61/100, MeanErr=92.350984(6.26%), 32.46M WeightUpdates/sec
Iter:62/100, MeanErr=93.208298(0.93%), 31.08M WeightUpdates/sec
Iter:63/100, MeanErr=90.784723(-2.60%), 21.19M WeightUpdates/sec
Iter:64/100, MeanErr=88.685225(-2.31%), 33.17M WeightUpdates/sec
Iter:65/100, MeanErr=91.668555(3.36%), 30.65M WeightUpdates/sec
Iter:66/100, MeanErr=82.607568(-9.88%), 29.72M WeightUpdates/sec
Iter:67/100, MeanErr=88.787842(7.48%), 32.98M WeightUpdates/sec
Iter:68/100, MeanErr=88.793186(0.01%), 34.67M WeightUpdates/sec
Iter:69/100, MeanErr=88.918795(0.14%), 14.09M WeightUpdates/sec
Iter:70/100, MeanErr=87.121434(-2.02%), 33.02M WeightUpdates/sec
Iter:71/100, MeanErr=86.865602(-0.29%), 34.87M WeightUpdates/sec
Iter:72/100, MeanErr=87.261979(0.46%), 32.34M WeightUpdates/sec
Iter:73/100, MeanErr=87.812460(0.63%), 31.35M WeightUpdates/sec
Iter:74/100, MeanErr=87.818462(0.01%), 32.54M WeightUpdates/sec
Iter:75/100, MeanErr=87.085672(-0.83%), 34.80M WeightUpdates/sec
Iter:76/100, MeanErr=85.773668(-1.51%), 35.39M WeightUpdates/sec
Iter:77/100, MeanErr=85.338703(-0.51%), 34.59M WeightUpdates/sec
Iter:78/100, MeanErr=79.370105(-6.99%), 30.14M WeightUpdates/sec
Iter:79/100, MeanErr=83.026209(4.61%), 32.32M WeightUpdates/sec
Iter:80/100, MeanErr=89.776417(8.13%), 33.14M WeightUpdates/sec
Iter:81/100, MeanErr=85.447100(-4.82%), 32.32M WeightUpdates/sec
Iter:82/100, MeanErr=83.991969(-1.70%), 22.12M WeightUpdates/sec
Iter:83/100, MeanErr=85.065064(1.28%), 30.41M WeightUpdates/sec
Iter:84/100, MeanErr=83.762008(-1.53%), 31.29M WeightUpdates/sec
Iter:85/100, MeanErr=84.217726(0.54%), 34.92M WeightUpdates/sec
Iter:86/100, MeanErr=82.395181(-2.16%), 34.26M WeightUpdates/sec
Iter:87/100, MeanErr=82.979145(0.71%), 22.87M WeightUpdates/sec
Iter:88/100, MeanErr=83.656685(0.82%), 28.51M WeightUpdates/sec
Iter:89/100, MeanErr=81.132468(-3.02%), 32.43M WeightUpdates/sec
Iter:90/100, MeanErr=81.311106(0.22%), 30.91M WeightUpdates/sec
Iter:91/100, MeanErr=81.953897(0.79%), 31.98M WeightUpdates/sec
Iter:92/100, MeanErr=79.018074(-3.58%), 33.13M WeightUpdates/sec
Iter:93/100, MeanErr=78.220412(-1.01%), 31.47M WeightUpdates/sec
Iter:94/100, MeanErr=80.833884(3.34%), 25.16M WeightUpdates/sec
Iter:95/100, MeanErr=81.550135(0.89%), 32.64M WeightUpdates/sec
Iter:96/100, MeanErr=77.785628(-4.62%), 32.54M WeightUpdates/sec
Iter:97/100, MeanErr=76.438158(-1.73%), 34.34M WeightUpdates/sec
Iter:98/100, MeanErr=79.471621(3.97%), 33.12M WeightUpdates/sec
Iter:99/100, MeanErr=76.038475(-4.32%), 33.01M WeightUpdates/sec
Iter:100/100, MeanErr=75.349164(-0.91%), 32.68M WeightUpdates/sec
Done!
Estimated Post-training MeanError = 75.768932
___________________________________________________________________
Not training a calibrator because it is not needed.
Elapsed time: 00:00:00.1178557
Elapsed time: 00:00:00.0088299
Beginning processing data.
Rows Read: 8, Read Time: 0, Transform Time: 0
Beginning processing data.
Elapsed time: 00:00:00.0293893
Finished writing 8 rows.
Writing completed.
Rows Read: 5, Total Rows Processed: 5, Total Chunk Time: 0.001 seconds 
   rating      Score
0    82.0  70.120613
1    64.0  66.344688
2    68.0  68.862373
3    58.0  68.241341
4    63.0  67.196869

ottimizzatori

• microsoftml.adadelta_optimizer: metodo di frequenza di apprendimento adattivo

• microsoftml.sgd_optimizer: discesa del gradiente stocastico

matematiche

• microsoftml.avx_math: accelerazione con istruzioni AVX

• microsoftml.clr_math: accelerazione con funzioni matematiche .NET

• microsoftml.gpu_math: accelerazione con CUDA NVidia

• microsoftml.mkl_math: accelerazione con Intel MKL

• microsoftml.sse_math: accelerazione con istruzioni SSE