In [1]:

    #| default_exp model.optimization.nn.tsc.vittsc.pen_digits_evaluation_mask
    %load_ext autoreload
    %autoreload 2

In [2]:

    # declare a list tasks whose products you want to use as inputs
    upstream = ['tabular_to_timeseries_pen_digits', 'model_training_pen_digits']

In [3]:

    # Parameters
    upstream = {"model_training_pen_digits": {"nb": "/home/ubuntu/vitmtsc_nbdev/output/403_model.optimization.nn.tsc.vittsc.pen_digits_training_mask_tune.html", "PenDigits_MODEL_TUNE_OUTPUT": "/home/ubuntu/vitmtsc_nbdev/output/PenDigits/ray_results", "PenDigits_MODEL_TRAINING_OUTPUT": "/home/ubuntu/vitmtsc_nbdev/output/PenDigits/experiments_result", "PenDigits_MODEL_TRAINING_CHECKPOINT_OUTPUT": "/home/ubuntu/vitmtsc_nbdev/output/PenDigits/experiments_result/checkpoint", "PenDigits_BEST_MODEL": "/home/ubuntu/vitmtsc_nbdev/output/PenDigits/experiments_result/best_model.ckpt", "PenDigits_BEST_MODEL_CONFIG": "/home/ubuntu/vitmtsc_nbdev/output/PenDigits/experiments_result/best_model_config.json"}, "tabular_to_timeseries_pen_digits": {"nb": "/home/ubuntu/vitmtsc_nbdev/output/303_feature_preprocessing.pen_digits.tabular_to_timeseries.html", "PenDigits_TRAIN_MODEL_INPUT": "/home/ubuntu/vitmtsc_nbdev/output/PenDigits/target_encoding-nn/train", "PenDigits_VALID_MODEL_INPUT": "/home/ubuntu/vitmtsc_nbdev/output/PenDigits/target_encoding-nn/valid", "PenDigits_TEST_MODEL_INPUT": "/home/ubuntu/vitmtsc_nbdev/output/PenDigits/target_encoding-nn/test"}}
    product = {"nb": "/home/ubuntu/vitmtsc_nbdev/output/503_model.optimization.nn.tsc.vittsc.pen_digits_evaluation_mask.html", "PenDigits_MODEL_VALID_EVAL_OUTPUT": "/home/ubuntu/vitmtsc_nbdev/output/PenDigits/experiments_result/evaluation/valid", "PenDigits_MODEL_TEST_EVAL_OUTPUT": "/home/ubuntu/vitmtsc_nbdev/output/PenDigits/experiments_result/evaluation/test"}

In [4]:

    #| hide
    from nbdev.showdoc import *

In [5]:

    #| export
    import sys
    import pathlib as p

    def is_running_from_ipython():
        from IPython import get_ipython
        return get_ipython() is not None

    if not is_running_from_ipython() and __package__ is None:
        DIR = p.Path(__file__).resolve().parent
        sys.path.insert(0, str(DIR.parent))
        __package__ = DIR.name

In [6]:

    #| export
    from vitmtsc.model.optimization.nn.tsc.vittsc.pen_digits_training_mask_tune import *

    class_weight: [0.47730892 0.48191318 0.46982759 0.51327055 0.47428797 0.52865961
     0.5362254  0.48660714 0.52959364 0.51415094]

In [7]:

    #| export
    upstream = {
        "tabular_to_timeseries_pen_digits": {
            "nb": "/home/ubuntu/vitmtsc_nbdev/output/303_feature_preprocessing.pen_digits.tabular_to_timeseries.html",
            "PenDigits_TRAIN_MODEL_INPUT": "/home/ubuntu/vitmtsc_nbdev/output/PenDigits/target_encoding-nn/train",
            "PenDigits_VALID_MODEL_INPUT": "/home/ubuntu/vitmtsc_nbdev/output/PenDigits/target_encoding-nn/valid",
            "PenDigits_TEST_MODEL_INPUT": "/home/ubuntu/vitmtsc_nbdev/output/PenDigits/target_encoding-nn/test",
        },
        "model_training_pen_digits": {
            "nb": "/home/ubuntu/vitmtsc_nbdev/output/403_model.optimization.nn.tsc.vittsc.pen_digits_training_mask_tune.html",
            "PenDigits_MODEL_TUNE_OUTPUT": "/home/ubuntu/vitmtsc_nbdev/output/PenDigits/ray_results",
            "PenDigits_MODEL_TRAINING_OUTPUT": "/home/ubuntu/vitmtsc_nbdev/output/PenDigits/experiments_result",
            "PenDigits_MODEL_TRAINING_CHECKPOINT_OUTPUT": "/home/ubuntu/vitmtsc_nbdev/output/PenDigits/experiments_result/checkpoint",
            "PenDigits_BEST_MODEL": "/home/ubuntu/vitmtsc_nbdev/output/PenDigits/experiments_result/best_model.ckpt",
            "PenDigits_BEST_MODEL_CONFIG": "/home/ubuntu/vitmtsc_nbdev/output/PenDigits/experiments_result/best_model_config.json",
        },
    }
    product = {
        "nb": "/home/ubuntu/vitmtsc_nbdev/output/503_model.optimization.nn.tsc.vittsc.pen_digits_evaluation_mask.html",
        "PenDigits_MODEL_VALID_EVAL_OUTPUT": "/home/ubuntu/vitmtsc_nbdev/output/PenDigits/experiments_result/evaluation/valid",
        "PenDigits_MODEL_TEST_EVAL_OUTPUT": "/home/ubuntu/vitmtsc_nbdev/output/PenDigits/experiments_result/evaluation/test",
    }

In [8]:

    # |export
    import json
    def get_best_model_config():
        with open(upstream['model_training_pen_digits']['PenDigits_BEST_MODEL_CONFIG'], 'r') as json_file:
            return json.load(json_file)

In [9]:

    #| export
    import pandas as pd
    import os
    import torch
    import math
    import glob

    import pytorch_lightning as pl
    from torch.nn import functional as F
    import matplotlib.pyplot as plt
    import scikitplot as skplt
    from pytorch_lightning import LightningModule
    from pytorch_lightning import Trainer
    from petastorm import make_batch_reader
    from petastorm.pytorch import DataLoader

## Vision Transformer for Multivariate Time-Series Classification (VitMTSC) Model with Masking - Evaluation¶

> Load Model

> Model Evaluation: Evaluate Model on test and validation dataset using PR-AUC

In [10]:

    #| export
    DATASET_NAME = 'PenDigits'

    VALID_DATA_DIR = f"file://{upstream['tabular_to_timeseries_pen_digits']['PenDigits_VALID_MODEL_INPUT']}"
    TEST_DATA_DIR = f"file://{upstream['tabular_to_timeseries_pen_digits']['PenDigits_TEST_MODEL_INPUT']}"
    VALID_EVAL_OUTPUT_DIR = product['PenDigits_MODEL_VALID_EVAL_OUTPUT']
    TEST_EVAL_OUTPUT_DIR = product['PenDigits_MODEL_TEST_EVAL_OUTPUT']
    BEST_MODEL_CHECKPOINT = upstream['model_training_pen_digits']['PenDigits_BEST_MODEL']
    NUM_WORKERS=1
    SHARD_COUNT=1
    BATCH_SIZE = 64
    TOTAL_VALID_BATCHES = math.ceil(get_valid_dataset_size()/BATCH_SIZE)
    TOTAL_TEST_BATCHES = math.ceil(get_test_dataset_size()/BATCH_SIZE)
    BEST_MODEL_CHECKPOINT, TOTAL_VALID_BATCHES, TOTAL_TEST_BATCHES, VALID_DATA_DIR, TEST_DATA_DIR, VALID_EVAL_OUTPUT_DIR, TEST_EVAL_OUTPUT_DIR

Out[10]:

    ('/home/ubuntu/vitmtsc_nbdev/output/PenDigits/experiments_result/best_model.ckpt',
     94,
     55,
     'file:///home/ubuntu/vitmtsc_nbdev/output/PenDigits/target_encoding-nn/valid',
     'file:///home/ubuntu/vitmtsc_nbdev/output/PenDigits/target_encoding-nn/test',
     '/home/ubuntu/vitmtsc_nbdev/output/PenDigits/experiments_result/evaluation/valid',
     '/home/ubuntu/vitmtsc_nbdev/output/PenDigits/experiments_result/evaluation/test')

In [11]:

    !mkdir -p $VALID_EVAL_OUTPUT_DIR
    !mkdir -p $TEST_EVAL_OUTPUT_DIR

## 1. VitMTSC Classification Prediction Task¶

In [12]:

    #| export
    class VitMTSCClassificationPredictionTask(LightningModule):
        def __init__(self,
                     model,
                     output_pred_dir,
                     input_data_dir,
                     batch_size=BATCH_SIZE,
                     num_workers=NUM_WORKERS,
                     shard_count = SHARD_COUNT):
            super().__init__()
            pl.seed_everything(42, workers=True)
            self.model = model
            self.case_id = []
            self.probability_0 = []
            self.probability_1 = []
            self.probability_2 = []
            self.probability_3 = []
            self.probability_4 = []
            self.probability_5 = []
            self.probability_6 = []
            self.probability_7 = []
            self.probability_8 = []
            self.probability_9 = []
            self.prediction = []
            self.target = []
            self.output_pred_dir = output_pred_dir
            self.input_data_dir = input_data_dir
            self.prediction_files = input_data_dir
            self.batch_size = batch_size
            self.num_workers = num_workers
            self.shard_count = shard_count

        def test_step(self, batch, batch_idx):
            x, y, case_id_1, mask = batch
            y_hat = self.model(x, mask)
            self.case_id.extend(case_id_1.to('cpu').numpy())
            self.probability_0.extend(F.softmax(y_hat, dim=1)[:,0].to('cpu').numpy())
            self.probability_1.extend(F.softmax(y_hat, dim=1)[:,1].to('cpu').numpy())
            self.probability_2.extend(F.softmax(y_hat, dim=1)[:,2].to('cpu').numpy())
            self.probability_3.extend(F.softmax(y_hat, dim=1)[:,3].to('cpu').numpy())
            self.probability_4.extend(F.softmax(y_hat, dim=1)[:,4].to('cpu').numpy())
            self.probability_5.extend(F.softmax(y_hat, dim=1)[:,5].to('cpu').numpy())
            self.probability_6.extend(F.softmax(y_hat, dim=1)[:,6].to('cpu').numpy())
            self.probability_7.extend(F.softmax(y_hat, dim=1)[:,7].to('cpu').numpy())
            self.probability_8.extend(F.softmax(y_hat, dim=1)[:,8].to('cpu').numpy())
            self.probability_9.extend(F.softmax(y_hat, dim=1)[:,9].to('cpu').numpy())
            self.prediction.extend(torch.max(y_hat.data, 1)[1].to('cpu').numpy())
            self.target.extend(y.to('cpu').numpy())

        def test_dataloader(self):
            print('test_dataloader: local rank :', int(os.environ['LOCAL_RANK']), 'shard count: ', self.shard_count)
            self.test_ds = make_batch_reader(self.prediction_files, workers_count=self.num_workers,
                                             cur_shard = int(os.environ['LOCAL_RANK']),
                                             shard_count = self.shard_count, num_epochs = 2)
            return DataLoader(self.test_ds, batch_size = self.batch_size, collate_fn= petastorm_collate_fn)

        def test_epoch_end(self, outputs):
            print('Consolidating predictions on GPU:', os.environ['LOCAL_RANK'])
            df_text_predictions = pd.DataFrame({'case_id': self.case_id,
                                                'probability_0': self.probability_0,
                                                'probability_1': self.probability_1,
                                                'probability_2': self.probability_2,
                                                'probability_3': self.probability_3,
                                                'probability_4': self.probability_4,
                                                'probability_5': self.probability_5,
                                                'probability_6': self.probability_6,
                                                'probability_7': self.probability_7,
                                                'probability_8': self.probability_8,
                                                'probability_9': self.probability_9,
                                                'prediction': self.prediction,
                                                'target': self.target
                                                })
            print('Writing predictions on GPU:', os.environ['LOCAL_RANK'])
            df_text_predictions.to_csv(self.output_pred_dir + "/" + os.environ['LOCAL_RANK'] + '_predictions.csv', index=False)
            print('Finished Writing predictions on GPU:', os.environ['LOCAL_RANK'])

In [13]:

    #| export
    def get_model_for_prediction(BEST_MODEL_CHECKPOINT, config, output_pred_dir, input_data_dir, shard_count = SHARD_COUNT):
        # load the best model
        pl.seed_everything(42, workers=True)
        model = VitTimeSeriesTransformer.load_from_checkpoint(BEST_MODEL_CHECKPOINT, config = config)
        model.eval()
        return VitMTSCClassificationPredictionTask(model = model, shard_count = shard_count, output_pred_dir = output_pred_dir, input_data_dir = input_data_dir)

In [14]:

    #| export
    def write_prediction_for_valid_dataset(BEST_MODEL_CHECKPOINT,
                                          config,
                                          shard_count,
                                          output_pred_dir = VALID_EVAL_OUTPUT_DIR,
                                          input_data_dir=VALID_DATA_DIR):
        pl.seed_everything(42, workers=True)
        model = get_model_for_prediction(BEST_MODEL_CHECKPOINT = BEST_MODEL_CHECKPOINT,
                                         config = config,
                                         shard_count = shard_count,
                                         output_pred_dir = output_pred_dir,
                                         input_data_dir = input_data_dir)
        trainer = Trainer(gpus = [0],
                          accelerator='dp',
                          progress_bar_refresh_rate=1,
                          limit_test_batches = TOTAL_VALID_BATCHES)
        trainer.test(model)

    def write_prediction_for_test_dataset(BEST_MODEL_CHECKPOINT,
                                          config,
                                          shard_count,
                                          output_pred_dir = TEST_EVAL_OUTPUT_DIR,
                                          input_data_dir=TEST_DATA_DIR):
        pl.seed_everything(42, workers=True)
        model = get_model_for_prediction(BEST_MODEL_CHECKPOINT = BEST_MODEL_CHECKPOINT,
                                         config = config,
                                         shard_count = shard_count,
                                         output_pred_dir = output_pred_dir,
                                         input_data_dir = input_data_dir)
        trainer = Trainer(gpus = [0],
                          accelerator='dp',
                          progress_bar_refresh_rate=1,
                          limit_test_batches = TOTAL_TEST_BATCHES)
        trainer.test(model)

In [15]:

    %env LOCAL_RANK=0

    env: LOCAL_RANK=0

In [16]:

    #| export
    if __name__ == "__main__":
        print('Processing valid dataset...\n')
        write_prediction_for_valid_dataset(BEST_MODEL_CHECKPOINT = BEST_MODEL_CHECKPOINT,
                                          config = get_best_model_config(),
                                          shard_count = SHARD_COUNT)
        print('Finished Processing valid dataset!!!\n')

        print('Processing test dataset...\n')
        write_prediction_for_test_dataset(BEST_MODEL_CHECKPOINT = BEST_MODEL_CHECKPOINT,
                                          config = get_best_model_config(),
                                          shard_count = SHARD_COUNT)
        print('Finished Processing test dataset!!!\n')

    Global seed set to 42
    Global seed set to 42
    Global seed set to 42
    /home/ubuntu/anaconda3/envs/rapids-22.08_ploomber/lib/python3.8/site-packages/pytorch_lightning/trainer/connectors/accelerator_connector.py:297: LightningDeprecationWarning: Passing `Trainer(accelerator='dp')` has been deprecated in v1.5 and will be removed in v1.7. Use `Trainer(strategy='dp')` instead.
      rank_zero_deprecation(
    /home/ubuntu/anaconda3/envs/rapids-22.08_ploomber/lib/python3.8/site-packages/pytorch_lightning/loops/utilities.py:91: PossibleUserWarning: `max_epochs` was not set. Setting it to 1000 epochs. To train without an epoch limit, set `max_epochs=-1`.
      rank_zero_warn(
    /home/ubuntu/anaconda3/envs/rapids-22.08_ploomber/lib/python3.8/site-packages/pytorch_lightning/trainer/connectors/callback_connector.py:96: LightningDeprecationWarning: Setting `Trainer(progress_bar_refresh_rate=1)` is deprecated in v1.5 and will be removed in v1.7. Please pass `pytorch_lightning.callbacks.progress.TQDMProgressBar` with `refresh_rate` directly to the Trainer's `callbacks` argument instead. Or, to disable the progress bar pass `enable_progress_bar = False` to the Trainer.
      rank_zero_deprecation(
    GPU available: True, used: True
    TPU available: False, using: 0 TPU cores
    IPU available: False, using: 0 IPUs
    HPU available: False, using: 0 HPUs
    Missing logger folder: /home/ubuntu/vitmtsc_nbdev/lightning_logs

    Processing valid dataset...

    LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3]

    test_dataloader: local rank : 0 shard count:  1

    /home/ubuntu/anaconda3/envs/rapids-22.08_ploomber/lib/python3.8/site-packages/petastorm/fs_utils.py:88: FutureWarning: pyarrow.localfs is deprecated as of 2.0.0, please use pyarrow.fs.LocalFileSystem instead.
      self._filesystem = pyarrow.localfs
    /home/ubuntu/anaconda3/envs/rapids-22.08_ploomber/lib/python3.8/site-packages/petastorm/etl/dataset_metadata.py:402: FutureWarning: Specifying the 'metadata_nthreads' argument is deprecated as of pyarrow 8.0.0, and the argument will be removed in a future version
      dataset = pq.ParquetDataset(path_or_paths, filesystem=fs, validate_schema=False, metadata_nthreads=10)
    /home/ubuntu/anaconda3/envs/rapids-22.08_ploomber/lib/python3.8/site-packages/petastorm/etl/dataset_metadata.py:362: FutureWarning: 'ParquetDataset.common_metadata' attribute is deprecated as of pyarrow 5.0.0 and will be removed in a future version.
      if not dataset.common_metadata:
    /home/ubuntu/anaconda3/envs/rapids-22.08_ploomber/lib/python3.8/site-packages/petastorm/reader.py:405: FutureWarning: Specifying the 'metadata_nthreads' argument is deprecated as of pyarrow 8.0.0, and the argument will be removed in a future version
      self.dataset = pq.ParquetDataset(dataset_path, filesystem=pyarrow_filesystem,
    /home/ubuntu/anaconda3/envs/rapids-22.08_ploomber/lib/python3.8/site-packages/petastorm/unischema.py:317: FutureWarning: 'ParquetDataset.pieces' attribute is deprecated as of pyarrow 5.0.0 and will be removed in a future version. Specify 'use_legacy_dataset=False' while constructing the ParquetDataset, and then use the '.fragments' attribute instead.
      meta = parquet_dataset.pieces[0].get_metadata()
    /home/ubuntu/anaconda3/envs/rapids-22.08_ploomber/lib/python3.8/site-packages/petastorm/unischema.py:321: FutureWarning: 'ParquetDataset.partitions' attribute is deprecated as of pyarrow 5.0.0 and will be removed in a future version. Specify 'use_legacy_dataset=False' while constructing the ParquetDataset, and then use the '.partitioning' attribute instead.
      for partition in (parquet_dataset.partitions or []):
    /home/ubuntu/anaconda3/envs/rapids-22.08_ploomber/lib/python3.8/site-packages/petastorm/etl/dataset_metadata.py:253: FutureWarning: 'ParquetDataset.metadata' attribute is deprecated as of pyarrow 5.0.0 and will be removed in a future version.
      metadata = dataset.metadata
    /home/ubuntu/anaconda3/envs/rapids-22.08_ploomber/lib/python3.8/site-packages/petastorm/etl/dataset_metadata.py:254: FutureWarning: 'ParquetDataset.common_metadata' attribute is deprecated as of pyarrow 5.0.0 and will be removed in a future version.
      common_metadata = dataset.common_metadata
    /home/ubuntu/anaconda3/envs/rapids-22.08_ploomber/lib/python3.8/site-packages/petastorm/etl/dataset_metadata.py:350: FutureWarning: 'ParquetDataset.pieces' attribute is deprecated as of pyarrow 5.0.0 and will be removed in a future version. Specify 'use_legacy_dataset=False' while constructing the ParquetDataset, and then use the '.fragments' attribute instead.
      futures_list = [thread_pool.submit(_split_piece, piece, dataset.fs.open) for piece in dataset.pieces]
    /home/ubuntu/anaconda3/envs/rapids-22.08_ploomber/lib/python3.8/site-packages/petastorm/etl/dataset_metadata.py:350: FutureWarning: 'ParquetDataset.fs' attribute is deprecated as of pyarrow 5.0.0 and will be removed in a future version. Specify 'use_legacy_dataset=False' while constructing the ParquetDataset, and then use the '.filesystem' attribute instead.
      futures_list = [thread_pool.submit(_split_piece, piece, dataset.fs.open) for piece in dataset.pieces]
    /home/ubuntu/anaconda3/envs/rapids-22.08_ploomber/lib/python3.8/site-packages/petastorm/etl/dataset_metadata.py:334: FutureWarning: ParquetDatasetPiece is deprecated as of pyarrow 5.0.0 and will be removed in a future version.
      return [pq.ParquetDatasetPiece(piece.path, open_file_func=fs_open,
    /home/ubuntu/anaconda3/envs/rapids-22.08_ploomber/lib/python3.8/site-packages/petastorm/arrow_reader_worker.py:138: FutureWarning: 'ParquetDataset.fs' attribute is deprecated as of pyarrow 5.0.0 and will be removed in a future version. Specify 'use_legacy_dataset=False' while constructing the ParquetDataset, and then use the '.filesystem' attribute instead.
      parquet_file = ParquetFile(self._dataset.fs.open(piece.path))
    /home/ubuntu/anaconda3/envs/rapids-22.08_ploomber/lib/python3.8/site-packages/petastorm/arrow_reader_worker.py:286: FutureWarning: 'ParquetDataset.partitions' attribute is deprecated as of pyarrow 5.0.0 and will be removed in a future version. Specify 'use_legacy_dataset=False' while constructing the ParquetDataset, and then use the '.partitioning' attribute instead.
      partition_names = self._dataset.partitions.partition_names if self._dataset.partitions else set()
    /home/ubuntu/anaconda3/envs/rapids-22.08_ploomber/lib/python3.8/site-packages/petastorm/arrow_reader_worker.py:289: FutureWarning: 'ParquetDataset.partitions' attribute is deprecated as of pyarrow 5.0.0 and will be removed in a future version. Specify 'use_legacy_dataset=False' while constructing the ParquetDataset, and then use the '.partitioning' attribute instead.
      table = piece.read(columns=column_names - partition_names, partitions=self._dataset.partitions)

    Testing: 0it [00:00, ?it/s]

    Global seed set to 42
    Global seed set to 42
    Global seed set to 42
    /home/ubuntu/anaconda3/envs/rapids-22.08_ploomber/lib/python3.8/site-packages/pytorch_lightning/trainer/connectors/accelerator_connector.py:297: LightningDeprecationWarning: Passing `Trainer(accelerator='dp')` has been deprecated in v1.5 and will be removed in v1.7. Use `Trainer(strategy='dp')` instead.
      rank_zero_deprecation(
    /home/ubuntu/anaconda3/envs/rapids-22.08_ploomber/lib/python3.8/site-packages/pytorch_lightning/loops/utilities.py:91: PossibleUserWarning: `max_epochs` was not set. Setting it to 1000 epochs. To train without an epoch limit, set `max_epochs=-1`.
      rank_zero_warn(
    /home/ubuntu/anaconda3/envs/rapids-22.08_ploomber/lib/python3.8/site-packages/pytorch_lightning/trainer/connectors/callback_connector.py:96: LightningDeprecationWarning: Setting `Trainer(progress_bar_refresh_rate=1)` is deprecated in v1.5 and will be removed in v1.7. Please pass `pytorch_lightning.callbacks.progress.TQDMProgressBar` with `refresh_rate` directly to the Trainer's `callbacks` argument instead. Or, to disable the progress bar pass `enable_progress_bar = False` to the Trainer.
      rank_zero_deprecation(
    GPU available: True, used: True
    TPU available: False, using: 0 TPU cores
    IPU available: False, using: 0 IPUs
    HPU available: False, using: 0 HPUs
    LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3]

    Consolidating predictions on GPU: 0
    Writing predictions on GPU: 0
    Finished Writing predictions on GPU: 0
    Finished Processing valid dataset!!!

    Processing test dataset...

    test_dataloader: local rank : 0 shard count:  1

    /home/ubuntu/anaconda3/envs/rapids-22.08_ploomber/lib/python3.8/site-packages/petastorm/fs_utils.py:88: FutureWarning: pyarrow.localfs is deprecated as of 2.0.0, please use pyarrow.fs.LocalFileSystem instead.
      self._filesystem = pyarrow.localfs
    /home/ubuntu/anaconda3/envs/rapids-22.08_ploomber/lib/python3.8/site-packages/petastorm/etl/dataset_metadata.py:402: FutureWarning: Specifying the 'metadata_nthreads' argument is deprecated as of pyarrow 8.0.0, and the argument will be removed in a future version
      dataset = pq.ParquetDataset(path_or_paths, filesystem=fs, validate_schema=False, metadata_nthreads=10)
    /home/ubuntu/anaconda3/envs/rapids-22.08_ploomber/lib/python3.8/site-packages/petastorm/etl/dataset_metadata.py:362: FutureWarning: 'ParquetDataset.common_metadata' attribute is deprecated as of pyarrow 5.0.0 and will be removed in a future version.
      if not dataset.common_metadata:
    /home/ubuntu/anaconda3/envs/rapids-22.08_ploomber/lib/python3.8/site-packages/petastorm/reader.py:405: FutureWarning: Specifying the 'metadata_nthreads' argument is deprecated as of pyarrow 8.0.0, and the argument will be removed in a future version
      self.dataset = pq.ParquetDataset(dataset_path, filesystem=pyarrow_filesystem,
    /home/ubuntu/anaconda3/envs/rapids-22.08_ploomber/lib/python3.8/site-packages/petastorm/unischema.py:317: FutureWarning: 'ParquetDataset.pieces' attribute is deprecated as of pyarrow 5.0.0 and will be removed in a future version. Specify 'use_legacy_dataset=False' while constructing the ParquetDataset, and then use the '.fragments' attribute instead.
      meta = parquet_dataset.pieces[0].get_metadata()
    /home/ubuntu/anaconda3/envs/rapids-22.08_ploomber/lib/python3.8/site-packages/petastorm/unischema.py:321: FutureWarning: 'ParquetDataset.partitions' attribute is deprecated as of pyarrow 5.0.0 and will be removed in a future version. Specify 'use_legacy_dataset=False' while constructing the ParquetDataset, and then use the '.partitioning' attribute instead.
      for partition in (parquet_dataset.partitions or []):
    /home/ubuntu/anaconda3/envs/rapids-22.08_ploomber/lib/python3.8/site-packages/petastorm/etl/dataset_metadata.py:253: FutureWarning: 'ParquetDataset.metadata' attribute is deprecated as of pyarrow 5.0.0 and will be removed in a future version.
      metadata = dataset.metadata
    /home/ubuntu/anaconda3/envs/rapids-22.08_ploomber/lib/python3.8/site-packages/petastorm/etl/dataset_metadata.py:254: FutureWarning: 'ParquetDataset.common_metadata' attribute is deprecated as of pyarrow 5.0.0 and will be removed in a future version.
      common_metadata = dataset.common_metadata
    /home/ubuntu/anaconda3/envs/rapids-22.08_ploomber/lib/python3.8/site-packages/petastorm/etl/dataset_metadata.py:350: FutureWarning: 'ParquetDataset.pieces' attribute is deprecated as of pyarrow 5.0.0 and will be removed in a future version. Specify 'use_legacy_dataset=False' while constructing the ParquetDataset, and then use the '.fragments' attribute instead.
      futures_list = [thread_pool.submit(_split_piece, piece, dataset.fs.open) for piece in dataset.pieces]
    /home/ubuntu/anaconda3/envs/rapids-22.08_ploomber/lib/python3.8/site-packages/petastorm/etl/dataset_metadata.py:350: FutureWarning: 'ParquetDataset.fs' attribute is deprecated as of pyarrow 5.0.0 and will be removed in a future version. Specify 'use_legacy_dataset=False' while constructing the ParquetDataset, and then use the '.filesystem' attribute instead.
      futures_list = [thread_pool.submit(_split_piece, piece, dataset.fs.open) for piece in dataset.pieces]
    /home/ubuntu/anaconda3/envs/rapids-22.08_ploomber/lib/python3.8/site-packages/petastorm/etl/dataset_metadata.py:334: FutureWarning: ParquetDatasetPiece is deprecated as of pyarrow 5.0.0 and will be removed in a future version.
      return [pq.ParquetDatasetPiece(piece.path, open_file_func=fs_open,
    /home/ubuntu/anaconda3/envs/rapids-22.08_ploomber/lib/python3.8/site-packages/petastorm/arrow_reader_worker.py:138: FutureWarning: 'ParquetDataset.fs' attribute is deprecated as of pyarrow 5.0.0 and will be removed in a future version. Specify 'use_legacy_dataset=False' while constructing the ParquetDataset, and then use the '.filesystem' attribute instead.
      parquet_file = ParquetFile(self._dataset.fs.open(piece.path))
    /home/ubuntu/anaconda3/envs/rapids-22.08_ploomber/lib/python3.8/site-packages/petastorm/arrow_reader_worker.py:286: FutureWarning: 'ParquetDataset.partitions' attribute is deprecated as of pyarrow 5.0.0 and will be removed in a future version. Specify 'use_legacy_dataset=False' while constructing the ParquetDataset, and then use the '.partitioning' attribute instead.
      partition_names = self._dataset.partitions.partition_names if self._dataset.partitions else set()
    /home/ubuntu/anaconda3/envs/rapids-22.08_ploomber/lib/python3.8/site-packages/petastorm/arrow_reader_worker.py:289: FutureWarning: 'ParquetDataset.partitions' attribute is deprecated as of pyarrow 5.0.0 and will be removed in a future version. Specify 'use_legacy_dataset=False' while constructing the ParquetDataset, and then use the '.partitioning' attribute instead.
      table = piece.read(columns=column_names - partition_names, partitions=self._dataset.partitions)

    Testing: 0it [00:00, ?it/s]

    Consolidating predictions on GPU: 0
    Writing predictions on GPU: 0
    Finished Writing predictions on GPU: 0
    Finished Processing test dataset!!!

## 2. Valid Dataset Prediction Evaluation¶

In [17]:

    import scikitplot as skplt
    import matplotlib.pyplot as plt
    from sklearn.metrics import f1_score
    from sklearn.metrics import f1_score

    valid_gdf = pd.concat(map(pd.read_csv, glob.glob(f'{VALID_EVAL_OUTPUT_DIR}/*.csv')))
    valid_gdf['target'] = valid_gdf['target'].astype('int64')
    valid_gdf['case_id'] = valid_gdf['case_id'].astype('int64')
    valid_gdf = valid_gdf.drop_duplicates()
    valid_gdf

Out[17]:

| case_id | probability_0 | probability_1 | probability_2 | probability_3 | probability_4 | probability_5 | probability_6 | probability_7 | probability_8 | probability_9 | prediction | target
--- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ---
0 | 1058 | 0.000166 | 0.998261 | 2.286276e-04 | 0.000024 | 0.000112 | 0.000011 | 0.000177 | 0.000130 | 0.000011 | 0.000879 | 1 | 1
1 | 6385 | 0.002315 | 0.000215 | 2.023655e-05 | 0.000069 | 0.996016 | 0.000097 | 0.000470 | 0.000003 | 0.000270 | 0.000524 | 4 | 4
2 | 589 | 0.000025 | 0.001607 | 1.686597e-04 | 0.988019 | 0.000664 | 0.002243 | 0.000065 | 0.000020 | 0.000504 | 0.006686 | 3 | 3
3 | 4333 | 0.000013 | 0.000997 | 5.663831e-04 | 0.996973 | 0.000340 | 0.000167 | 0.000090 | 0.000012 | 0.000772 | 0.000069 | 3 | 3
4 | 954 | 0.000006 | 0.000268 | 1.630294e-03 | 0.996070 | 0.000302 | 0.000273 | 0.000040 | 0.000019 | 0.001298 | 0.000094 | 3 | 3
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ...
5990 | 529 | 0.000191 | 0.000014 | 7.946492e-06 | 0.000054 | 0.000092 | 0.997461 | 0.000424 | 0.000773 | 0.000319 | 0.000663 | 5 | 5
5991 | 5793 | 0.000064 | 0.000381 | 8.207239e-07 | 0.000098 | 0.000773 | 0.000231 | 0.000015 | 0.000037 | 0.000037 | 0.998364 | 9 | 9
5992 | 2856 | 0.001553 | 0.000047 | 8.179735e-06 | 0.000072 | 0.997188 | 0.000118 | 0.000592 | 0.000002 | 0.000242 | 0.000178 | 4 | 4
5993 | 3911 | 0.000006 | 0.000488 | 2.501828e-04 | 0.998382 | 0.000222 | 0.000213 | 0.000036 | 0.000005 | 0.000284 | 0.000113 | 3 | 3
5994 | 2643 | 0.000009 | 0.004174 | 9.939153e-01 | 0.000575 | 0.000033 | 0.000055 | 0.000377 | 0.000721 | 0.000140 | 0.000001 | 2 | 2

5995 rows × 13 columns

In [18]:

    valid_gdf[valid_gdf.prediction == valid_gdf.target].count()

Out[18]:

    case_id          5963
    probability_0    5963
    probability_1    5963
    probability_2    5963
    probability_3    5963
    probability_4    5963
    probability_5    5963
    probability_6    5963
    probability_7    5963
    probability_8    5963
    probability_9    5963
    prediction       5963
    target           5963
    dtype: int64

In [19]:

    valid_gdf['target'].min(), valid_gdf['prediction'].min(), valid_gdf['target'].max(), valid_gdf['prediction'].max()

Out[19]:

    (0, 0, 9, 9)

In [20]:

    skplt.metrics.plot_precision_recall(valid_gdf['target'].to_numpy(),
                                        valid_gdf[['probability_0', 'probability_1', 'probability_2', 'probability_3', 'probability_4',
                                                 'probability_5', 'probability_6', 'probability_7', 'probability_8', 'probability_9']].to_numpy(),
                                        cmap='nipy_spectral')
    plt.show()

In [21]:

    skplt.metrics.plot_roc(valid_gdf['target'].to_numpy(),
                           valid_gdf[['probability_0', 'probability_1', 'probability_2', 'probability_3', 'probability_4',
                                      'probability_5', 'probability_6', 'probability_7', 'probability_8', 'probability_9']].to_numpy(),
                           cmap='nipy_spectral')
    plt.show()

In [22]:

    f1_score(valid_gdf['target'], valid_gdf['prediction'], average='macro')

Out[22]:

    0.9947727404900883

In [23]:

    f1_score(valid_gdf['target'], valid_gdf['prediction'], average='weighted')

Out[23]:

    0.9946631280402078

## 3. Test Dataset Prediction Evaluation¶

In [24]:

    test_gdf = pd.concat(map(pd.read_csv, glob.glob(f'{TEST_EVAL_OUTPUT_DIR}/*.csv')))
    test_gdf['target'] = test_gdf['target'].astype('int64')
    test_gdf['case_id'] = test_gdf['case_id'].astype('int64')
    test_gdf = test_gdf.drop_duplicates()
    test_gdf

Out[24]:

| case_id | probability_0 | probability_1 | probability_2 | probability_3 | probability_4 | probability_5 | probability_6 | probability_7 | probability_8 | probability_9 | prediction | target
--- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ---
0 | 819 | 0.000568 | 0.000003 | 0.000020 | 0.000162 | 0.000025 | 0.000181 | 0.000017 | 0.000125 | 0.998855 | 0.000046 | 8 | 8
1 | 2500 | 0.000098 | 0.000004 | 0.000051 | 0.000339 | 0.000051 | 0.000112 | 0.000011 | 0.000047 | 0.999252 | 0.000033 | 8 | 8
2 | 2683 | 0.000009 | 0.000929 | 0.000306 | 0.997776 | 0.000154 | 0.000341 | 0.000037 | 0.000007 | 0.000302 | 0.000140 | 3 | 3
3 | 3266 | 0.000329 | 0.972163 | 0.019162 | 0.000075 | 0.000017 | 0.000117 | 0.001428 | 0.006386 | 0.000274 | 0.000048 | 1 | 1
4 | 2445 | 0.000005 | 0.000510 | 0.000505 | 0.998208 | 0.000182 | 0.000162 | 0.000038 | 0.000005 | 0.000310 | 0.000075 | 3 | 3
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ...
3493 | 3181 | 0.000004 | 0.000679 | 0.996775 | 0.001046 | 0.000040 | 0.000050 | 0.000300 | 0.000896 | 0.000209 | 0.000001 | 2 | 2
3494 | 2392 | 0.997786 | 0.000401 | 0.000030 | 0.000009 | 0.000752 | 0.000083 | 0.000209 | 0.000199 | 0.000347 | 0.000184 | 0 | 0
3495 | 1736 | 0.000165 | 0.000156 | 0.000189 | 0.000014 | 0.000259 | 0.000422 | 0.998451 | 0.000310 | 0.000031 | 0.000002 | 6 | 6
3496 | 1641 | 0.000327 | 0.000101 | 0.000240 | 0.000014 | 0.000620 | 0.000396 | 0.997998 | 0.000250 | 0.000050 | 0.000003 | 6 | 6
3497 | 2782 | 0.000284 | 0.998244 | 0.000577 | 0.000015 | 0.000010 | 0.000030 | 0.000362 | 0.000351 | 0.000027 | 0.000100 | 1 | 1

3498 rows × 13 columns

In [25]:

    test_gdf[test_gdf.prediction == test_gdf.target].count()

Out[25]:

    case_id          3402
    probability_0    3402
    probability_1    3402
    probability_2    3402
    probability_3    3402
    probability_4    3402
    probability_5    3402
    probability_6    3402
    probability_7    3402
    probability_8    3402
    probability_9    3402
    prediction       3402
    target           3402
    dtype: int64

In [26]:

    test_gdf['target'].min(), test_gdf['prediction'].min(), test_gdf['target'].max(), test_gdf['prediction'].max()

Out[26]:

    (0, 0, 9, 9)

In [27]:

    skplt.metrics.plot_precision_recall(test_gdf['target'].to_numpy(),
                                        test_gdf[['probability_0', 'probability_1', 'probability_2', 'probability_3', 'probability_4',
                                                 'probability_5', 'probability_6', 'probability_7', 'probability_8', 'probability_9']].to_numpy(),
                                        cmap='nipy_spectral')
    plt.show()

In [28]:

    skplt.metrics.plot_roc(test_gdf['target'].to_numpy(),
                           test_gdf[['probability_0', 'probability_1', 'probability_2', 'probability_3', 'probability_4',
                                     'probability_5', 'probability_6', 'probability_7', 'probability_8', 'probability_9']].to_numpy(),
                           cmap='nipy_spectral')
    plt.show()

In [29]:

    f1_score(test_gdf['target'], test_gdf['prediction'], average='macro')

Out[29]:

    0.9729983939882019

In [30]:

    f1_score(test_gdf['target'], test_gdf['prediction'], average='weighted')

Out[30]:

    0.972562580397056

We shutdown the kernel!!!

In [31]:

    from nbdev import nbdev_export
    nbdev_export()