#| default_exp model.optimization.nn.tsc.vittsc.pen_digits_evaluation_mask
%load_ext autoreload
%autoreload 2
# declare a list tasks whose products you want to use as inputs
upstream = ['tabular_to_timeseries_pen_digits', 'model_training_pen_digits']
# 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"}
#| hide
from nbdev.showdoc import *
#| 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
#| 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]
#| 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",
}
# |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)
#| 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
Load Model
Model Evaluation: Evaluate Model on test and validation dataset using PR-AUC
#| 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
('/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')
!mkdir -p $VALID_EVAL_OUTPUT_DIR
!mkdir -p $TEST_EVAL_OUTPUT_DIR
#| 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'])
#| 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)
#| 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)
%env LOCAL_RANK=0
env: LOCAL_RANK=0
#| 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!!!
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
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
valid_gdf[valid_gdf.prediction == valid_gdf.target].count()
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
valid_gdf['target'].min(), valid_gdf['prediction'].min(), valid_gdf['target'].max(), valid_gdf['prediction'].max()
(0, 0, 9, 9)
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()
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()
f1_score(valid_gdf['target'], valid_gdf['prediction'], average='macro')
0.9947727404900883
f1_score(valid_gdf['target'], valid_gdf['prediction'], average='weighted')
0.9946631280402078
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
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
test_gdf[test_gdf.prediction == test_gdf.target].count()
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
test_gdf['target'].min(), test_gdf['prediction'].min(), test_gdf['target'].max(), test_gdf['prediction'].max()
(0, 0, 9, 9)
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()
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()
f1_score(test_gdf['target'], test_gdf['prediction'], average='macro')
0.9729983939882019
f1_score(test_gdf['target'], test_gdf['prediction'], average='weighted')
0.972562580397056
We shutdown the kernel!!!
from nbdev import nbdev_export
nbdev_export()