Nitin Siwach

Which option to chose to set parameters to LightGBMClassifier()?

2

There are three ways to set pameters to a ```LightGBMClassifier()``` 1. In the class constructor itself 2. Using the ```params``` argument in the ```fit``` method 3. Using the ```setParams``` method...

setting useSingleDatasetMode to True gives : java: malloc.c:4033: _int_malloc: Assertion `(unsigned long) (size) >= (unsigned long) (nb)' failed.

2

setting ```useSingleDatasetMode``` to ```True``` gives ``` java: malloc.c:4033: _int_malloc: Assertion `(unsigned long) (size) >= (unsigned long) (nb)' failed. ``` I keep everything else the same and set that flag to...

Where to specify numClasses metadata in LightGBMClassifier?

2

My ```labelCol``` is named ```label``` Post running vectorassembler I run ```df_modeling = df_modeling.withColumn('label', col('label').alias('label',metadata={'numClasses':self.num_classes}))``` and yet I get the warning: ```com.microsoft.azure.synapse.ml.lightgbm.LightGBMClassifier: com.microsoft.azure.synapse.ml.lightgbm.LightGBMClassifier inferred 2 classes for labelCol=LightGBMClassifier_4f7ef3fb1833__labelCol since numClasses was...

LightGBMClassificationModel.fit gives raise Py4JNetworkError("Answer from Java side is empty") py4j.protocol.Py4JNetworkError: Answer from Java side is empty

1

**Describe the bug** ```LightGBMClassificationModel.fit``` Cannot handle too much data. Fails without even having to collect anything at the driver. I ```LightGBMClassificationModel.fit``` on data(10000,241) - It executes perfectly I ```LightGBMClassificationModel.fit``` on...

Making synapse.ml compatible with pandas_on_pyspark

2

With pyspark 3.2.0 now supporting ```pandas_on_pyspark``` (earlier koalas), this would make things much easier for a lot of us travelling from the faraway world of pandas. Currently, we need to...

How to track lightgbm eval performance during and post training?

1

One can specify the eval data and eval metric for lightgbm via the ```validationIndicatorCol``` and ```metric``` parameters. How can one: 1. See the iteration by iteration evaluation metric evolution like...

Bypass all callbacks, parameter validation when '--help' is requested on any command/subcommand

2

### First Check - [X] I added a very descriptive title to this issue. - [X] I used the GitHub search to find a similar issue and didn't find it....

mean-teacher has not [initialized](https://github.com/CuriousAI/mean-teacher/blob/master/tensorflow/mean_teacher/weight_norm.py#L41) b as given in Original [weight_norm paper](https://arxiv.org/pdf/1602.07868.pdf) and its [implementation](https://github.com/openai/pixel-cnn/blob/fc86dbce1d508fa79f8e9a7d1942d229249a5366/pixel_cnn_pp/nn.py) that you have [referenced](https://github.com/CuriousAI/mean-teacher/blob/master/tensorflow/mean_teacher/weight_norm.py#L13). Is it intentional?

I am talking about `self.mean_cons_cost_mt, self.cons_costs_mt = consistency_costs( self.cons_logits_1, self.class_logits_ema, self.cons_coefficient, consistency_mask, self.hyper['consistency_trust'])` What is the difference between using 'self.class_logits_ema' and using 'self.cons_logits_ema' as consistency targets?

Gradients because of ema being dependent upon student variables

1

Great paper! Tensorflow documentation says the EMA variables are created with (trainable=False) and added to the GraphKeys.ALL_VARIABLES collection. Now as they are not trainable they wont have the gradient applied...