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Small changes to make the US weather dataset work properly, example test runs in config file.

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Kevin Alberts 2021-01-29 10:52:49 +01:00
parent e4c51e2d3d
commit d0785e12e2
Signed by: Kurocon
GPG key ID: BCD496FEBA0C6BC1
4 changed files with 154 additions and 56 deletions
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151
config.example.py
View file

@ -3,14 +3,147 @@ DATASET_STORAGE_BASE_PATH = "/path/to/this/project/datasets"
TRAIN_TEMP_DATA_BASE_PATH = "/path/to/this/project/train_temp"
TEST_TEMP_DATA_BASE_PATH = "/path/to/this/project/test_temp"
TEST_RUNS = [
{
'name': "Basic test run",
'encoder_model': "models.base_encoder.BaseEncoder",
'encoder_kwargs': {},
'dataset_model': "models.base_dataset.BaseDataset",
'dataset_kwargs': {},
'corruption_model': "models.base_corruption.NoCorruption",
'corruption_kwargs': {},
},
# CIFAR-10 dataset
# {
# 'name': "CIFAR-10 on basic auto-encoder",
# 'encoder_model': "models.basic_encoder.BasicAutoEncoder",
# 'encoder_kwargs': {},
# 'dataset_model': "models.cifar10_dataset.Cifar10Dataset",
# 'dataset_kwargs': {"path": "cifar-10-batches-py"},
# 'corruption_model': "models.gaussian_corruption.GaussianCorruption",
# 'corruption_kwargs': {},
# },
# {
# 'name': "CIFAR-10 on sparse L1 auto-encoder",
# 'encoder_model': "models.sparse_encoder.SparseL1AutoEncoder",
# 'encoder_kwargs': {},
# 'dataset_model': "models.cifar10_dataset.Cifar10Dataset",
# 'dataset_kwargs': {"path": "cifar-10-batches-py"},
# 'corruption_model': "models.gaussian_corruption.GaussianCorruption",
# 'corruption_kwargs': {},
# },
# {
# 'name': "CIFAR-10 on denoising auto-encoder",
# 'encoder_model': "models.denoising_encoder.DenoisingAutoEncoder",
# 'encoder_kwargs': {'input_corruption_model': "models.gaussian_corruption.GaussianCorruption"},
# 'dataset_model': "models.cifar10_dataset.Cifar10Dataset",
# 'dataset_kwargs': {"path": "cifar-10-batches-py"},
# 'corruption_model': "models.gaussian_corruption.GaussianCorruption",
# 'corruption_kwargs': {},
# },
# {
# 'name': "CIFAR-10 on contractive auto-encoder",
# 'encoder_model': "models.contractive_encoder.ContractiveAutoEncoder",
# 'encoder_kwargs': {},
# 'dataset_model': "models.cifar10_dataset.Cifar10Dataset",
# 'dataset_kwargs': {"path": "cifar-10-batches-py"},
# 'corruption_model': "models.gaussian_corruption.GaussianCorruption",
# 'corruption_kwargs': {},
# },
# {
# 'name': "CIFAR-10 on variational auto-encoder",
# 'encoder_model': "models.variational_encoder.VariationalAutoEncoder",
# 'encoder_kwargs': {},
# 'dataset_model': "models.cifar10_dataset.Cifar10Dataset",
# 'dataset_kwargs': {"path": "cifar-10-batches-py"},
# 'corruption_model': "models.gaussian_corruption.GaussianCorruption",
# 'corruption_kwargs': {},
# },
# MNIST dataset
# {
# 'name': "MNIST on basic auto-encoder",
# 'encoder_model': "models.basic_encoder.BasicAutoEncoder",
# 'encoder_kwargs': {},
# 'dataset_model': "models.mnist_dataset.MNISTDataset",
# 'dataset_kwargs': {"path": "mnist"},
# 'corruption_model': "models.gaussian_corruption.GaussianCorruption",
# 'corruption_kwargs': {},
# },
# {
# 'name': "MNIST on sparse L1 auto-encoder",
# 'encoder_model': "models.sparse_encoder.SparseL1AutoEncoder",
# 'encoder_kwargs': {},
# 'dataset_model': "models.mnist_dataset.MNISTDataset",
# 'dataset_kwargs': {"path": "mnist"},
# 'corruption_model': "models.gaussian_corruption.GaussianCorruption",
# 'corruption_kwargs': {},
# },
# {
# 'name': "MNIST on denoising auto-encoder",
# 'encoder_model': "models.denoising_encoder.DenoisingAutoEncoder",
# 'encoder_kwargs': {'input_corruption_model': "models.gaussian_corruption.GaussianCorruption"},
# 'dataset_model': "models.mnist_dataset.MNISTDataset",
# 'dataset_kwargs': {"path": "mnist"},
# 'corruption_model': "models.gaussian_corruption.GaussianCorruption",
# 'corruption_kwargs': {},
# },
# {
# 'name': "MNIST on contractive auto-encoder",
# 'encoder_model': "models.contractive_encoder.ContractiveAutoEncoder",
# 'encoder_kwargs': {},
# 'dataset_model': "models.mnist_dataset.MNISTDataset",
# 'dataset_kwargs': {"path": "mnist"},
# 'corruption_model': "models.gaussian_corruption.GaussianCorruption",
# 'corruption_kwargs': {},
# },
# {
# 'name': "MNIST on variational auto-encoder",
# 'encoder_model': "models.variational_encoder.VariationalAutoEncoder",
# 'encoder_kwargs': {},
# 'dataset_model': "models.mnist_dataset.MNISTDataset",
# 'dataset_kwargs': {"path": "mnist"},
# 'corruption_model': "models.gaussian_corruption.GaussianCorruption",
# 'corruption_kwargs': {},
# },
# US Weather Events dataset
# {
# 'name': "US Weather Events on basic auto-encoder",
# 'encoder_model': "models.basic_encoder.BasicAutoEncoder",
# 'encoder_kwargs': {},
# 'dataset_model': "models.usweather_dataset.USWeatherEventsDataset",
# 'dataset_kwargs': {"path": "weather-events"},
# 'corruption_model': "models.random_corruption.RandomCorruption",
# 'corruption_kwargs': {},
# },
# {
# 'name': "US Weather Events on sparse L1 auto-encoder",
# 'encoder_model': "models.sparse_encoder.SparseL1AutoEncoder",
# 'encoder_kwargs': {},
# 'dataset_model': "models.usweather_dataset.USWeatherEventsDataset",
# 'dataset_kwargs': {"path": "weather-events"},
# 'corruption_model': "models.random_corruption.RandomCorruption",
# 'corruption_kwargs': {},
# },
# {
# 'name': "US Weather Events on denoising auto-encoder",
# 'encoder_model': "models.denoising_encoder.DenoisingAutoEncoder",
# 'encoder_kwargs': {'input_corruption_model': "models.random_corruption.RandomCorruption"},
# 'dataset_model': "models.usweather_dataset.USWeatherEventsDataset",
# 'dataset_kwargs': {"path": "weather-events"},
# 'corruption_model': "models.random_corruption.RandomCorruption",
# 'corruption_kwargs': {},
# },
# {
# 'name': "US Weather Events on contractive auto-encoder",
# 'encoder_model': "models.contractive_encoder.ContractiveAutoEncoder",
# 'encoder_kwargs': {},
# 'dataset_model': "models.usweather_dataset.USWeatherEventsDataset",
# 'dataset_kwargs': {"path": "weather-events"},
# 'corruption_model': "models.random_corruption.RandomCorruption",
# 'corruption_kwargs': {},
# },
# {
# 'name': "US Weather Events on variational auto-encoder",
# 'encoder_model': "models.variational_encoder.VariationalAutoEncoder",
# 'encoder_kwargs': {},
# 'dataset_model': "models.usweather_dataset.USWeatherEventsDataset",
# 'dataset_kwargs': {"path": "weather-events"},
# 'corruption_model': "models.random_corruption.RandomCorruption",
# 'corruption_kwargs': {},
# },
]

2
main.py
View file

@ -44,7 +44,7 @@ def run_tests():
test_run = TestRun(dataset=dataset, encoder=encoder, corruption=corruption)
# Run TestRun
test_run.run(retrain=True)
test_run.run(retrain=False)
# Cleanup to avoid out-of-memory situations when running lots of tests
del test_run

3
models/contractive_encoder.py
View file

@ -73,7 +73,8 @@ class ContractiveAutoEncoder(BaseEncoder):
weights = self.state_dict()['encoder.2.weight']
# Hadamard product
hidden_output = hidden_output.reshape(hidden_output.shape[0], hidden_output.shape[2])
if len(hidden_output.shape) > 2:
hidden_output = hidden_output.reshape(hidden_output.shape[0], hidden_output.shape[2])
dh = hidden_output * (1 - hidden_output)
# Sum through input dimension to improve efficiency (suggested in reference)

54
models/usweather_dataset.py
View file

@ -1,7 +1,6 @@
import csv
import os
from collections import defaultdict
from datetime import datetime
from typing import Optional
@ -26,31 +25,22 @@ class USWeatherLoss(_Loss):
def forward(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
losses = []
start = 0
length = len(self.dataset._labels['Type'])
# Type is 1-hot encoded, so use cross entropy loss
losses.append(self.ce_loss(input[start:start+length], torch.argmax(target[start:start+length].long(), dim=1)))
losses.append(self.ce_loss(input[:, start:start+length], torch.argmax(target[:, start:start+length].long(), dim=1)))
start += length
length = len(self.dataset._labels['Severity'])
# Severity is 1-hot encoded, so use cross entropy loss
losses.append(self.ce_loss(input[start:start+length], torch.argmax(target[start:start+length].long(), dim=1)))
losses.append(self.ce_loss(input[:, start:start+length], torch.argmax(target[:, start:start+length].long(), dim=1)))
start += length
# Start time is a number, so use L1 loss
losses.append(self.l1_loss(input[start], target[start]))
# End time is a number, so use L1 loss
losses.append(self.l1_loss(input[start + 1], target[start + 1]))
start += 2
length = len(self.dataset._labels['TimeZone'])
# TimeZone is 1-hot encoded, so use cross entropy loss
losses.append(self.ce_loss(input[start:start+length], torch.argmax(target[start:start+length].long(), dim=1)))
losses.append(self.ce_loss(input[:, start:start+length], torch.argmax(target[:, start:start+length].long(), dim=1)))
start += length
# Location latitude is a number, so use L1 loss
losses.append(self.l1_loss(input[start], target[start]))
# Location longitude is a number, so use L1 loss
losses.append(self.l1_loss(input[start + 1], target[start + 1]))
start += 2
length = len(self.dataset._labels['State'])
# State is 1-hot encoded, so use cross entropy loss
losses.append(self.ce_loss(input[start:start+length], torch.argmax(target[start:start+length].long(), dim=1)))
losses.append(self.ce_loss(input[:, start:start+length], torch.argmax(target[:, start:start+length].long(), dim=1)))
return sum(losses)
@ -110,23 +100,9 @@ class USWeatherEventsDataset(BaseDataset):
# 1-hot encoded event severity columns
[int(row['Severity'] == self._labels['Severity'][i]) for i in range(len(self._labels['Severity']))] +
[
# Start time as unix timestamp
datetime.strptime(row['StartTime(UTC)'], "%Y-%m-%d %H:%M:%S").timestamp(),
# End time as unix timestamp
datetime.strptime(row['EndTime(UTC)'], "%Y-%m-%d %H:%M:%S").timestamp()
] +
# 1-hot encoded event timezone columns
[int(row['TimeZone'] == self._labels['TimeZone'][i]) for i in range(len(self._labels['TimeZone']))] +
[
# Location Latitude as float
float(row['LocationLat']),
# Location Longitude as float
float(row['LocationLng']),
] +
# 1-hot encoded event state columns
[int(row['State'] == self._labels['State'][i]) for i in range(len(self._labels['State']))]
@ -151,7 +127,7 @@ class USWeatherEventsDataset(BaseDataset):
# train_data, test_data = self._data[:2500000], self._data[2500000:]
# Speed up training a bit
train_data, test_data = self._data[:50000], self._data[100000:150000]
train_data, test_data = self._data[:250000], self._data[250000:500000]
self._trainset = self.__class__.get_new(name=f"{self.name} Training", data=train_data, labels=self._labels,
source_path=self._source_path)
@ -167,13 +143,11 @@ class USWeatherEventsDataset(BaseDataset):
size = 0
size += len(labels['Type'])
size += len(labels['Severity'])
size += 2
size += len(labels['TimeZone'])
size += 2
size += len(labels['State'])
return size
else:
return 69
return 65
def __getitem__(self, item):
data = self._data[item]
@ -196,15 +170,9 @@ class USWeatherEventsDataset(BaseDataset):
length = len(self._labels['Severity'])
severities = output[start:start+length]
start += length
start_time = output[start]
end_time = output[start+1]
start += 2
length = len(self._labels['TimeZone'])
timezones = output[start:start+length]
start += length
location_lat = output[start]
location_lng = output[start+1]
start += 2
length = len(self._labels['State'])
states = output[start:start+length]
@ -214,14 +182,10 @@ class USWeatherEventsDataset(BaseDataset):
timezone = self._labels['TimeZone'][timezones.index(max(timezones))]
state = self._labels['State'][states.index(max(states))]
# Convert timestamp float into string time
start_time = datetime.fromtimestamp(start_time).strftime("%Y-%m-%d %H:%M:%S")
end_time = datetime.fromtimestamp(end_time).strftime("%Y-%m-%d %H:%M:%S")
return [event_type, severity, start_time, end_time, timezone, location_lat, location_lng, state]
return [event_type, severity, timezone, state]
def save_batch_to_sample(self, batch, filename):
res = ["Type,Severity,StartTime(UTC),EndTime(UTC),TimeZone,LocationLat,LocationLng,State\n"]
res = ["Type,Severity,TimeZone,State\n"]
for row in batch:
row = row.tolist()