From d0785e12e26313d931863519bec3b0c766b2603c Mon Sep 17 00:00:00 2001
From: Kevin Alberts <kevin@kevinalberts.nl>
Date: Fri, 29 Jan 2021 10:52:49 +0100
Subject: [PATCH] Small changes to make the US weather dataset work properly,
example test runs in config file.
---
config.example.py | 151 ++++++++++++++++++++++++++++++++--
main.py | 2 +-
models/contractive_encoder.py | 3 +-
models/usweather_dataset.py | 54 ++----------
4 files changed, 154 insertions(+), 56 deletions(-)
diff --git a/config.example.py b/config.example.py
index ef885de..162a62c 100644
--- a/config.example.py
+++ b/config.example.py
@@ -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': {},
+ # },
]
+
diff --git a/main.py b/main.py
index 294f460..c204bb6 100644
--- a/main.py
+++ b/main.py
@@ -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
diff --git a/models/contractive_encoder.py b/models/contractive_encoder.py
index eeadb98..b128dce 100644
--- a/models/contractive_encoder.py
+++ b/models/contractive_encoder.py
@@ -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)
diff --git a/models/usweather_dataset.py b/models/usweather_dataset.py
index 6d71f06..9e1ec04 100644
--- a/models/usweather_dataset.py
+++ b/models/usweather_dataset.py
@@ -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()