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Train sampling #134

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Train sampling #134

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e4e700f
Construct truly random batches for windowed training instead of a sli…
Katsutoshii Jul 18, 2024
5391d0e
Merge branch 'train-notebook' of https://github.com/UrbanSystemsLab/c…
Katsutoshii Jul 18, 2024
1ee855a
walt fixes
waltaskew Jul 19, 2024
4a3943e
walt fixes
waltaskew Jul 19, 2024
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188 changes: 94 additions & 94 deletions usl_models/notebooks/train_flood_model.ipynb
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -1,95 +1,95 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Flood Model Training Notebook\n",
"\n",
"Train a Flood ConvLSTM Model using `usl_models` lib."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%load_ext autoreload\n",
"%autoreload 2\n",
"import tensorflow as tf\n",
"from usl_models.flood_ml import constants\n",
"from usl_models.flood_ml.model import FloodModel, FloodModelParams\n",
"from usl_models.flood_ml.dataset import load_dataset_windowed, load_dataset\n",
"\n",
"sim_names=[\"Manhattan-config_v1/Rainfall_Data_1.txt\"]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"dataset = load_dataset_windowed(sim_names=sim_names, batch_size=4)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model = FloodModel(FloodModelParams())\n",
"dataset = load_dataset_windowed(sim_names=sim_names, batch_size=4)\n",
"model.fit(dataset, epochs=1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Test calling the model on some data.\n",
"inputs, labels_ = next(iter(dataset))\n",
"prediction = model.call(inputs)\n",
"prediction.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Test calling the model for n predictions\n",
"full_dataset = load_dataset(sim_names=sim_names, batch_size=1)\n",
"inputs, labels_ = next(iter(full_dataset))\n",
"predictions = model.call_n(inputs, n=4)\n",
"predictions.shape"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "base",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.14"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Flood Model Training Notebook\n",
"\n",
"Train a Flood ConvLSTM Model using `usl_models` lib."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%load_ext autoreload\n",
"%autoreload 2\n",
"import tensorflow as tf\n",
"from usl_models.flood_ml import constants\n",
"from usl_models.flood_ml.model import FloodModel, FloodModelParams\n",
"from usl_models.flood_ml.dataset import load_dataset_windowed, load_dataset\n",
"\n",
"sim_names=[\"Manhattan-config_v1/Rainfall_Data_1.txt\"]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"dataset = load_dataset_windowed(sim_names=sim_names, batch_size=4)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model = FloodModel(FloodModelParams())\n",
"dataset = load_dataset_windowed(sim_names=sim_names, batch_size=4)\n",
"model.fit(dataset, epochs=1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Test calling the model on some data.\n",
"inputs, labels_ = next(iter(dataset))\n",
"prediction = model.call(inputs)\n",
"prediction.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Test calling the model for n predictions\n",
"full_dataset = load_dataset(sim_names=sim_names, batch_size=1)\n",
"inputs, labels_ = next(iter(full_dataset))\n",
"predictions = model.call_n(inputs, n=4)\n",
"predictions.shape"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "base",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.14"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
22 changes: 20 additions & 2 deletions usl_models/tests/flood_ml/test_dataset.py
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Original file line number Diff line number Diff line change
Expand Up @@ -18,7 +18,8 @@ def test_load_dataset_full(mock_metastore) -> None:

# Set some URLs to return from our metastore functions.
mock_metastore.get_temporal_feature_metadata.return_value = {
"as_vector_gcs_uri": "gs://temporal-features/temporal-feature.npy"
"as_vector_gcs_uri": "gs://temporal-features/temporal-feature.npy",
"rainfall_duration": 4,
}
mock_metastore.get_spatial_feature_and_label_chunk_metadata.return_value = [
(
Expand Down Expand Up @@ -73,8 +74,16 @@ def mock_blob_func(blob_name):
[
mock.call.bucket("temporal-features"),
mock.call.bucket().blob("temporal-feature.npy"),
]
)
mock_storage_client.assert_has_calls(
[
mock.call.bucket("spatial-features"),
mock.call.bucket().blob("spatial-feature.npy"),
]
)
mock_storage_client.assert_has_calls(
[
mock.call.bucket("labels"),
mock.call.bucket().blob("labels.npy"),
]
Expand Down Expand Up @@ -131,7 +140,8 @@ def test_load_dataset_windowed(mock_metastore) -> None:

# Set some URLs to return from our metastore functions.
mock_metastore.get_temporal_feature_metadata.return_value = {
"as_vector_gcs_uri": "gs://temporal-features/temporal-feature.npy"
"as_vector_gcs_uri": "gs://temporal-features/temporal-feature.npy",
"rainfall_duration": 4,
}
mock_metastore.get_spatial_feature_and_label_chunk_metadata.return_value = [
(
Expand Down Expand Up @@ -186,8 +196,16 @@ def mock_blob_func(blob_name):
[
mock.call.bucket("temporal-features"),
mock.call.bucket().blob("temporal-feature.npy"),
]
)
mock_storage_client.assert_has_calls(
[
mock.call.bucket("spatial-features"),
mock.call.bucket().blob("spatial-feature.npy"),
]
)
mock_storage_client.assert_has_calls(
[
mock.call.bucket("labels"),
mock.call.bucket().blob("labels.npy"),
]
Expand Down
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