From 105f2b072daf77e7c558acaa05c9682d0d70d265 Mon Sep 17 00:00:00 2001
From: jh-206 <jhirschi206@gmail.com>
Date: Sun, 13 Oct 2024 17:34:39 -0600
Subject: [PATCH] use data wrapper

---
 .../fmda_rnn_test_batch_reset_schedule.ipynb       | 54 ++++++++++++++--------
 1 file changed, 34 insertions(+), 20 deletions(-)

diff --git a/fmda/test_notebooks/fmda_rnn_test_batch_reset_schedule.ipynb b/fmda/test_notebooks/fmda_rnn_test_batch_reset_schedule.ipynb
index 6a1606f..1c6aff9 100644
--- a/fmda/test_notebooks/fmda_rnn_test_batch_reset_schedule.ipynb
+++ b/fmda/test_notebooks/fmda_rnn_test_batch_reset_schedule.ipynb
@@ -22,7 +22,7 @@
     "import sys\n",
     "sys.path.append('..')\n",
     "from moisture_rnn_pkl import pkl2train\n",
-    "from moisture_rnn import RNNParams, RNNData, RNN \n",
+    "from moisture_rnn import RNNParams, RNNData, RNN, rnn_data_wrap\n",
     "from utils import hash2, read_yml, read_pkl, retrieve_url, print_dict_summary, print_first, str2time, logging_setup\n",
     "from moisture_rnn import RNN\n",
     "import reproducibility\n",
@@ -54,7 +54,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "filename = \"fmda_nw_202401-05_f05.pkl\"\n",
+    "filename = \"fmda_rocky_202403-05_f05.pkl\"\n",
     "retrieve_url(\n",
     "    url = f\"https://demo.openwfm.org/web/data/fmda/dicts/{filename}\", \n",
     "    dest_path = f\"../data/{filename}\")"
@@ -93,7 +93,7 @@
    "outputs": [],
    "source": [
     "params_data.update({\n",
-    "    'hours': 3000,\n",
+    "    'hours': 2205,\n",
     "    'max_intp_time': 12,\n",
     "    'zero_lag_threshold': 12\n",
     "})\n",
@@ -131,7 +131,8 @@
     "               'bmax': params_data['hours'], # Upper bound of hidden state batch reset, using max hours\n",
     "               'features_list': ['Ed', 'Ew', 'rain', 'elev', 'lon', 'lat', 'solar', 'wind'],\n",
     "               'timesteps': 12,\n",
-    "               'batch_size': 50\n",
+    "               'batch_size': 50,\n",
+    "               'space_fracs': [.8, .1, .1]\n",
     "              })"
    ]
   },
@@ -150,26 +151,39 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# train_sp = combine_nested(train)\n",
-    "rnn_dat = RNNData(\n",
-    "    train, # input dictionary\n",
-    "    scaler=\"standard\",  # data scaling type\n",
-    "    features_list = params['features_list'] # features for predicting outcome\n",
-    ")\n",
+    "# # train_sp = combine_nested(train)\n",
+    "# rnn_dat = RNNData(\n",
+    "#     train, # input dictionary\n",
+    "#     scaler=\"standard\",  # data scaling type\n",
+    "#     features_list = params['features_list'] # features for predicting outcome\n",
+    "# )\n",
     "\n",
     "\n",
-    "rnn_dat.train_test_split(   \n",
-    "    time_fracs = [.9, .05, .05], # Percent of total time steps used for train/val/test\n",
-    "    space_fracs = [.40, .30, .30] # Percent of total timeseries used for train/val/test\n",
-    ")\n",
-    "rnn_dat.scale_data()\n",
+    "# rnn_dat.train_test_split(   \n",
+    "#     time_fracs = [.9, .05, .05], # Percent of total time steps used for train/val/test\n",
+    "#     space_fracs = [.40, .30, .30] # Percent of total timeseries used for train/val/test\n",
+    "# )\n",
+    "# rnn_dat.scale_data()\n",
     "\n",
-    "rnn_dat.batch_reshape(\n",
-    "    timesteps = params['timesteps'], # Timesteps aka sequence length for RNN input data. \n",
-    "    batch_size = params['batch_size'], # Number of samples of length timesteps for a single round of grad. descent\n",
-    "    start_times = np.zeros(len(rnn_dat.loc['train_locs']))\n",
-    ")\n",
+    "# rnn_dat.batch_reshape(\n",
+    "#     timesteps = params['timesteps'], # Timesteps aka sequence length for RNN input data. \n",
+    "#     batch_size = params['batch_size'], # Number of samples of length timesteps for a single round of grad. descent\n",
+    "#     start_times = np.zeros(len(rnn_dat.loc['train_locs']))\n",
+    "# )\n",
     "\n",
+    "# params.update({\n",
+    "#     'loc_batch_reset': rnn_dat.n_seqs # Used to reset hidden state when location changes for a given batch\n",
+    "# })"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "adbba43e-603b-4801-8a35-35b8ccc053af",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rnn_dat = rnn_data_wrap(train, params)\n",
     "params.update({\n",
     "    'loc_batch_reset': rnn_dat.n_seqs # Used to reset hidden state when location changes for a given batch\n",
     "})"
-- 
2.11.4.GIT