From acb172711dcaf2736b81be19f4ab3755a6c15d2b Mon Sep 17 00:00:00 2001
From: SurajSSingh <surajss@uci.edu>
Date: Wed, 11 May 2022 12:00:23 -0700
Subject: [PATCH] Added Export Simple Data
---
tf_model.ipynb | 339 ++++++++++++++++++++++++++++++++-----------------
1 file changed, 222 insertions(+), 117 deletions(-)
diff --git a/tf_model.ipynb b/tf_model.ipynb
index 4404a8c..0043c5f 100644
--- a/tf_model.ipynb
+++ b/tf_model.ipynb
@@ -16,7 +16,7 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
@@ -30,7 +30,7 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 2,
"metadata": {},
"outputs": [
{
@@ -47,7 +47,7 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
@@ -58,7 +58,7 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
@@ -241,7 +241,7 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
@@ -251,7 +251,7 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
@@ -266,7 +266,7 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
@@ -279,7 +279,7 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
@@ -289,7 +289,7 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 9,
"metadata": {},
"outputs": [
{
@@ -325,7 +325,7 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 10,
"metadata": {},
"outputs": [
{
@@ -602,7 +602,7 @@
"[551042 rows x 13 columns]"
]
},
- "execution_count": 12,
+ "execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
@@ -613,7 +613,7 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 37,
"metadata": {},
"outputs": [],
"source": [
@@ -623,7 +623,7 @@
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": 32,
"metadata": {},
"outputs": [
{
@@ -669,7 +669,54 @@
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": 38,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "sleep_data.to_csv(\".data/sleep_data_simple.csv\", index=False, index_label=False)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Model Development"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Setup"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "TEST_SIZE = 122\n",
+ "VALIDATION_SIZE = 183\n",
+ "\n",
+ "BATCH_SIZE = 32\n",
+ "INPUT_TIME_STEP = 10 # in minutes\n",
+ "INPUT_FEATURES_SIZE = 7\n",
+ "MAX_EPOCHS = 20"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "sleep_data = pd.read_csv(\".data/sleep_data_simple.csv\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 35,
"metadata": {},
"outputs": [
{
@@ -693,6 +740,7 @@
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
+ " <th>Unnamed: 0</th>\n",
" <th>sleep_id</th>\n",
" <th>minutes_since_begin</th>\n",
" <th>stage_start_hour</th>\n",
@@ -708,6 +756,7 @@
" <th>0</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
+ " <td>0</td>\n",
" <td>8</td>\n",
" <td>18</td>\n",
" <td>1.0</td>\n",
@@ -717,6 +766,7 @@
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
+ " <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>8</td>\n",
@@ -728,6 +778,7 @@
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
+ " <td>2</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>8</td>\n",
@@ -739,6 +790,7 @@
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
+ " <td>3</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>8</td>\n",
@@ -750,6 +802,7 @@
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
+ " <td>4</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>8</td>\n",
@@ -769,9 +822,11 @@
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
+ " <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>551037</th>\n",
+ " <td>551037</td>\n",
" <td>1132</td>\n",
" <td>426</td>\n",
" <td>13</td>\n",
@@ -783,6 +838,7 @@
" </tr>\n",
" <tr>\n",
" <th>551038</th>\n",
+ " <td>551038</td>\n",
" <td>1132</td>\n",
" <td>427</td>\n",
" <td>13</td>\n",
@@ -794,6 +850,7 @@
" </tr>\n",
" <tr>\n",
" <th>551039</th>\n",
+ " <td>551039</td>\n",
" <td>1132</td>\n",
" <td>428</td>\n",
" <td>13</td>\n",
@@ -805,6 +862,7 @@
" </tr>\n",
" <tr>\n",
" <th>551040</th>\n",
+ " <td>551040</td>\n",
" <td>1132</td>\n",
" <td>429</td>\n",
" <td>13</td>\n",
@@ -816,6 +874,7 @@
" </tr>\n",
" <tr>\n",
" <th>551041</th>\n",
+ " <td>551041</td>\n",
" <td>1132</td>\n",
" <td>430</td>\n",
" <td>13</td>\n",
@@ -827,53 +886,53 @@
" </tr>\n",
" </tbody>\n",
"</table>\n",
- "<p>551042 rows × 8 columns</p>\n",
+ "<p>551042 rows × 9 columns</p>\n",
"</div>"
],
"text/plain": [
- " sleep_id minutes_since_begin stage_start_hour stage_start_minute \\\n",
- "0 0 0 8 18 \n",
- "1 0 1 8 19 \n",
- "2 0 2 8 20 \n",
- "3 0 3 8 21 \n",
- "4 0 4 8 22 \n",
- "... ... ... ... ... \n",
- "551037 1132 426 13 17 \n",
- "551038 1132 427 13 18 \n",
- "551039 1132 428 13 19 \n",
- "551040 1132 429 13 20 \n",
- "551041 1132 430 13 21 \n",
+ " Unnamed: 0 sleep_id minutes_since_begin stage_start_hour \\\n",
+ "0 0 0 0 8 \n",
+ "1 1 0 1 8 \n",
+ "2 2 0 2 8 \n",
+ "3 3 0 3 8 \n",
+ "4 4 0 4 8 \n",
+ "... ... ... ... ... \n",
+ "551037 551037 1132 426 13 \n",
+ "551038 551038 1132 427 13 \n",
+ "551039 551039 1132 428 13 \n",
+ "551040 551040 1132 429 13 \n",
+ "551041 551041 1132 430 13 \n",
"\n",
- " awake_probability rem_probability light_probability \\\n",
- "0 1.0 0.0 0.0 \n",
- "1 1.0 0.0 0.0 \n",
- "2 1.0 0.0 0.0 \n",
- "3 1.0 0.0 0.0 \n",
- "4 1.0 0.0 0.0 \n",
- "... ... ... ... \n",
- "551037 0.0 0.0 1.0 \n",
- "551038 0.0 0.0 1.0 \n",
- "551039 0.0 0.0 1.0 \n",
- "551040 0.0 0.0 1.0 \n",
- "551041 0.0 0.0 1.0 \n",
+ " stage_start_minute awake_probability rem_probability \\\n",
+ "0 18 1.0 0.0 \n",
+ "1 19 1.0 0.0 \n",
+ "2 20 1.0 0.0 \n",
+ "3 21 1.0 0.0 \n",
+ "4 22 1.0 0.0 \n",
+ "... ... ... ... \n",
+ "551037 17 0.0 0.0 \n",
+ "551038 18 0.0 0.0 \n",
+ "551039 19 0.0 0.0 \n",
+ "551040 20 0.0 0.0 \n",
+ "551041 21 0.0 0.0 \n",
"\n",
- " deep_probability \n",
- "0 0.0 \n",
- "1 0.0 \n",
- "2 0.0 \n",
- "3 0.0 \n",
- "4 0.0 \n",
- "... ... \n",
- "551037 0.0 \n",
- "551038 0.0 \n",
- "551039 0.0 \n",
- "551040 0.0 \n",
- "551041 0.0 \n",
+ " light_probability deep_probability \n",
+ "0 0.0 0.0 \n",
+ "1 0.0 0.0 \n",
+ "2 0.0 0.0 \n",
+ "3 0.0 0.0 \n",
+ "4 0.0 0.0 \n",
+ "... ... ... \n",
+ "551037 1.0 0.0 \n",
+ "551038 1.0 0.0 \n",
+ "551039 1.0 0.0 \n",
+ "551040 1.0 0.0 \n",
+ "551041 1.0 0.0 \n",
"\n",
- "[551042 rows x 8 columns]"
+ "[551042 rows x 9 columns]"
]
},
- "execution_count": 15,
+ "execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
@@ -882,13 +941,6 @@
"sleep_data"
]
},
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Model Development"
- ]
- },
{
"cell_type": "markdown",
"metadata": {},
@@ -896,18 +948,6 @@
"### Helper functions and class"
]
},
- {
- "cell_type": "code",
- "execution_count": 16,
- "metadata": {},
- "outputs": [],
- "source": [
- "TEST_SIZE = 122\n",
- "VALIDATION_SIZE = 183\n",
- "TIME_STEP_INPUT = 10 # in minutes\n",
- "BATCH_SIZE = 32"
- ]
- },
{
"cell_type": "code",
"execution_count": 17,
@@ -921,13 +961,13 @@
},
{
"cell_type": "code",
- "execution_count": 31,
+ "execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"# Adapted from https://www.tensorflow.org/tutorials/structured_data/time_series\n",
"class WindowGenerator():\n",
- " def __init__(self, data, index: str = \"sleep_id\", input_width: int = TIME_STEP_INPUT, validation_size: int = VALIDATION_SIZE, test_size: int = TEST_SIZE, input_feature_slice: slice = slice(1,100), label_feature_slice: slice = slice(-4,100), generate_data_now: bool = True):\n",
+ " def __init__(self, data, index: str = \"sleep_id\", input_width: int = INPUT_TIME_STEP, validation_size: int = VALIDATION_SIZE, test_size: int = TEST_SIZE, input_feature_slice: slice = slice(1,100), label_feature_slice: slice = slice(-4,100), generate_data_now: bool = True):\n",
" # Partition data\n",
" self.training, self.testing = training_test_split_by_unique_index(sleep_data, index, test_size)\n",
" self.training, self.validation = training_test_split_by_unique_index(self.training, index, validation_size)\n",
@@ -952,9 +992,9 @@
" self.sample_ds = self.make_dataset(sleep_data[sleep_data[index] == 0])\n",
"\n",
" if generate_data_now:\n",
- " self.training_ds = self.make_dataset(self.training)\n",
- " self.validation_ds = self.make_dataset(self.validation)\n",
- " self.testing_ds = self.make_dataset(self.testing)\n",
+ " self.training_ds = self.make_dataset(self.training, index)\n",
+ " self.validation_ds = self.make_dataset(self.validation, index)\n",
+ " self.testing_ds = self.make_dataset(self.testing, index)\n",
"\n",
"\n",
" def __repr__(self):\n",
@@ -966,9 +1006,9 @@
"\n",
" def split_window(self, features):\n",
" inputs = features[:, self.input_slice, self.input_feature_slice]\n",
- " labels = features[:, self.labels_slice, self.label_feature_slice]\n",
+ " labels = tf.squeeze(features[:, self.labels_slice, self.label_feature_slice])\n",
" inputs.set_shape([None, self.input_width, None])\n",
- " labels.set_shape([None, self.label_width, None])\n",
+ " # labels.set_shape([None, self.label_width, None])\n",
" return inputs, labels\n",
"\n",
" def make_dataset(self, data, index_group: str = \"sleep_id\", sort_by: str = \"minutes_since_begin\"):\n",
@@ -1019,21 +1059,31 @@
},
{
"cell_type": "code",
- "execution_count": 32,
+ "execution_count": 25,
"metadata": {},
"outputs": [
{
- "data": {
- "text/plain": [
- "WindowGenerator:\n",
- "\tTotal window size: 11\n",
- "\tInput indices: [0 1 2 3 4 5 6 7 8 9]\n",
- "\tLabel indices: [10]"
- ]
- },
- "execution_count": 32,
- "metadata": {},
- "output_type": "execute_result"
+ "ename": "KeyError",
+ "evalue": "'sleep_id'",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)",
+ "File \u001b[0;32m~/Documents/School Folder/CS 437/Lab/Final Project/venv/lib/python3.10/site-packages/pandas/core/indexes/base.py:3621\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m <a href='file:///Users/nowadmin/Documents/School%20Folder/CS%20437/Lab/Final%20Project/venv/lib/python3.10/site-packages/pandas/core/indexes/base.py?line=3619'>3620</a>\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m-> <a href='file:///Users/nowadmin/Documents/School%20Folder/CS%20437/Lab/Final%20Project/venv/lib/python3.10/site-packages/pandas/core/indexes/base.py?line=3620'>3621</a>\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_engine\u001b[39m.\u001b[39;49mget_loc(casted_key)\n\u001b[1;32m <a href='file:///Users/nowadmin/Documents/School%20Folder/CS%20437/Lab/Final%20Project/venv/lib/python3.10/site-packages/pandas/core/indexes/base.py?line=3621'>3622</a>\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m \u001b[39mas\u001b[39;00m err:\n",
+ "File \u001b[0;32mpandas/_libs/index.pyx:136\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
+ "File \u001b[0;32mpandas/_libs/index.pyx:163\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
+ "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:5198\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
+ "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:5206\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
+ "\u001b[0;31mKeyError\u001b[0m: 'sleep_id'",
+ "\nThe above exception was the direct cause of the following exception:\n",
+ "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)",
+ "\u001b[1;32m/Users/nowadmin/Documents/School Folder/CS 437/Lab/Final Project/tf_model.ipynb Cell 33'\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> <a href='vscode-notebook-cell:/Users/nowadmin/Documents/School%20Folder/CS%20437/Lab/Final%20Project/tf_model.ipynb#ch0000072?line=0'>1</a>\u001b[0m wg \u001b[39m=\u001b[39m WindowGenerator(sleep_data)\n\u001b[1;32m <a href='vscode-notebook-cell:/Users/nowadmin/Documents/School%20Folder/CS%20437/Lab/Final%20Project/tf_model.ipynb#ch0000072?line=1'>2</a>\u001b[0m wg\n",
+ "\u001b[1;32m/Users/nowadmin/Documents/School Folder/CS 437/Lab/Final Project/tf_model.ipynb Cell 31'\u001b[0m in \u001b[0;36mWindowGenerator.__init__\u001b[0;34m(self, data, index, input_width, validation_size, test_size, input_feature_slice, label_feature_slice, generate_data_now)\u001b[0m\n\u001b[1;32m <a href='vscode-notebook-cell:/Users/nowadmin/Documents/School%20Folder/CS%20437/Lab/Final%20Project/tf_model.ipynb#ch0000065?line=2'>3</a>\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m__init__\u001b[39m(\u001b[39mself\u001b[39m, data, index: \u001b[39mstr\u001b[39m \u001b[39m=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39msleep_id\u001b[39m\u001b[39m\"\u001b[39m, input_width: \u001b[39mint\u001b[39m \u001b[39m=\u001b[39m INPUT_TIME_STEP, validation_size: \u001b[39mint\u001b[39m \u001b[39m=\u001b[39m VALIDATION_SIZE, test_size: \u001b[39mint\u001b[39m \u001b[39m=\u001b[39m TEST_SIZE, input_feature_slice: \u001b[39mslice\u001b[39m \u001b[39m=\u001b[39m \u001b[39mslice\u001b[39m(\u001b[39m1\u001b[39m,\u001b[39m100\u001b[39m), label_feature_slice: \u001b[39mslice\u001b[39m \u001b[39m=\u001b[39m \u001b[39mslice\u001b[39m(\u001b[39m-\u001b[39m\u001b[39m4\u001b[39m,\u001b[39m100\u001b[39m), generate_data_now: \u001b[39mbool\u001b[39m \u001b[39m=\u001b[39m \u001b[39mTrue\u001b[39;00m):\n\u001b[1;32m <a href='vscode-notebook-cell:/Users/nowadmin/Documents/School%20Folder/CS%20437/Lab/Final%20Project/tf_model.ipynb#ch0000065?line=3'>4</a>\u001b[0m \u001b[39m# Partition data\u001b[39;00m\n\u001b[0;32m----> <a href='vscode-notebook-cell:/Users/nowadmin/Documents/School%20Folder/CS%20437/Lab/Final%20Project/tf_model.ipynb#ch0000065?line=4'>5</a>\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtraining, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtesting \u001b[39m=\u001b[39m training_test_split_by_unique_index(sleep_data, index, test_size)\n\u001b[1;32m <a href='vscode-notebook-cell:/Users/nowadmin/Documents/School%20Folder/CS%20437/Lab/Final%20Project/tf_model.ipynb#ch0000065?line=5'>6</a>\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtraining, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mvalidation \u001b[39m=\u001b[39m training_test_split_by_unique_index(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtraining, index, validation_size)\n\u001b[1;32m <a href='vscode-notebook-cell:/Users/nowadmin/Documents/School%20Folder/CS%20437/Lab/Final%20Project/tf_model.ipynb#ch0000065?line=7'>8</a>\u001b[0m \u001b[39m# Window paramters\u001b[39;00m\n",
+ "\u001b[1;32m/Users/nowadmin/Documents/School Folder/CS 437/Lab/Final Project/tf_model.ipynb Cell 30'\u001b[0m in \u001b[0;36mtraining_test_split_by_unique_index\u001b[0;34m(data, index, test_size)\u001b[0m\n\u001b[1;32m <a href='vscode-notebook-cell:/Users/nowadmin/Documents/School%20Folder/CS%20437/Lab/Final%20Project/tf_model.ipynb#ch0000066?line=0'>1</a>\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mtraining_test_split_by_unique_index\u001b[39m(data, index: \u001b[39mstr\u001b[39m, test_size: \u001b[39mint\u001b[39m \u001b[39m=\u001b[39m \u001b[39m10\u001b[39m):\n\u001b[0;32m----> <a href='vscode-notebook-cell:/Users/nowadmin/Documents/School%20Folder/CS%20437/Lab/Final%20Project/tf_model.ipynb#ch0000066?line=1'>2</a>\u001b[0m test_ids \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39mrandom\u001b[39m.\u001b[39mchoice(data[index]\u001b[39m.\u001b[39munique(), size \u001b[39m=\u001b[39m test_size, replace\u001b[39m=\u001b[39m\u001b[39mFalse\u001b[39;00m)\n\u001b[1;32m <a href='vscode-notebook-cell:/Users/nowadmin/Documents/School%20Folder/CS%20437/Lab/Final%20Project/tf_model.ipynb#ch0000066?line=2'>3</a>\u001b[0m \u001b[39mreturn\u001b[39;00m data[\u001b[39m~\u001b[39mdata[index]\u001b[39m.\u001b[39misin(test_ids)], data[data[index]\u001b[39m.\u001b[39misin(test_ids)]\n",
+ "File \u001b[0;32m~/Documents/School Folder/CS 437/Lab/Final Project/venv/lib/python3.10/site-packages/pandas/core/frame.py:3505\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m <a href='file:///Users/nowadmin/Documents/School%20Folder/CS%20437/Lab/Final%20Project/venv/lib/python3.10/site-packages/pandas/core/frame.py?line=3502'>3503</a>\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcolumns\u001b[39m.\u001b[39mnlevels \u001b[39m>\u001b[39m \u001b[39m1\u001b[39m:\n\u001b[1;32m <a href='file:///Users/nowadmin/Documents/School%20Folder/CS%20437/Lab/Final%20Project/venv/lib/python3.10/site-packages/pandas/core/frame.py?line=3503'>3504</a>\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_getitem_multilevel(key)\n\u001b[0;32m-> <a href='file:///Users/nowadmin/Documents/School%20Folder/CS%20437/Lab/Final%20Project/venv/lib/python3.10/site-packages/pandas/core/frame.py?line=3504'>3505</a>\u001b[0m indexer \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mcolumns\u001b[39m.\u001b[39;49mget_loc(key)\n\u001b[1;32m <a href='file:///Users/nowadmin/Documents/School%20Folder/CS%20437/Lab/Final%20Project/venv/lib/python3.10/site-packages/pandas/core/frame.py?line=3505'>3506</a>\u001b[0m \u001b[39mif\u001b[39;00m is_integer(indexer):\n\u001b[1;32m <a href='file:///Users/nowadmin/Documents/School%20Folder/CS%20437/Lab/Final%20Project/venv/lib/python3.10/site-packages/pandas/core/frame.py?line=3506'>3507</a>\u001b[0m indexer \u001b[39m=\u001b[39m [indexer]\n",
+ "File \u001b[0;32m~/Documents/School Folder/CS 437/Lab/Final Project/venv/lib/python3.10/site-packages/pandas/core/indexes/base.py:3623\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m <a href='file:///Users/nowadmin/Documents/School%20Folder/CS%20437/Lab/Final%20Project/venv/lib/python3.10/site-packages/pandas/core/indexes/base.py?line=3620'>3621</a>\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_engine\u001b[39m.\u001b[39mget_loc(casted_key)\n\u001b[1;32m <a href='file:///Users/nowadmin/Documents/School%20Folder/CS%20437/Lab/Final%20Project/venv/lib/python3.10/site-packages/pandas/core/indexes/base.py?line=3621'>3622</a>\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m \u001b[39mas\u001b[39;00m err:\n\u001b[0;32m-> <a href='file:///Users/nowadmin/Documents/School%20Folder/CS%20437/Lab/Final%20Project/venv/lib/python3.10/site-packages/pandas/core/indexes/base.py?line=3622'>3623</a>\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mKeyError\u001b[39;00m(key) \u001b[39mfrom\u001b[39;00m \u001b[39merr\u001b[39;00m\n\u001b[1;32m <a href='file:///Users/nowadmin/Documents/School%20Folder/CS%20437/Lab/Final%20Project/venv/lib/python3.10/site-packages/pandas/core/indexes/base.py?line=3623'>3624</a>\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mTypeError\u001b[39;00m:\n\u001b[1;32m <a href='file:///Users/nowadmin/Documents/School%20Folder/CS%20437/Lab/Final%20Project/venv/lib/python3.10/site-packages/pandas/core/indexes/base.py?line=3624'>3625</a>\u001b[0m \u001b[39m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[1;32m <a href='file:///Users/nowadmin/Documents/School%20Folder/CS%20437/Lab/Final%20Project/venv/lib/python3.10/site-packages/pandas/core/indexes/base.py?line=3625'>3626</a>\u001b[0m \u001b[39m# InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[1;32m <a href='file:///Users/nowadmin/Documents/School%20Folder/CS%20437/Lab/Final%20Project/venv/lib/python3.10/site-packages/pandas/core/indexes/base.py?line=3626'>3627</a>\u001b[0m \u001b[39m# the TypeError.\u001b[39;00m\n\u001b[1;32m <a href='file:///Users/nowadmin/Documents/School%20Folder/CS%20437/Lab/Final%20Project/venv/lib/python3.10/site-packages/pandas/core/indexes/base.py?line=3627'>3628</a>\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_check_indexing_error(key)\n",
+ "\u001b[0;31mKeyError\u001b[0m: 'sleep_id'"
+ ]
}
],
"source": [
@@ -1043,7 +1093,7 @@
},
{
"cell_type": "code",
- "execution_count": 33,
+ "execution_count": 60,
"metadata": {},
"outputs": [],
"source": [
@@ -1052,7 +1102,7 @@
},
{
"cell_type": "code",
- "execution_count": 34,
+ "execution_count": 61,
"metadata": {},
"outputs": [],
"source": [
@@ -1061,26 +1111,16 @@
},
{
"cell_type": "code",
- "execution_count": 37,
+ "execution_count": 63,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "(array([[18., 8., 36., 1., 0., 0., 0.],\n",
- " [19., 8., 37., 1., 0., 0., 0.],\n",
- " [20., 8., 38., 1., 0., 0., 0.],\n",
- " [21., 8., 39., 1., 0., 0., 0.],\n",
- " [22., 8., 40., 1., 0., 0., 0.],\n",
- " [23., 8., 41., 1., 0., 0., 0.],\n",
- " [24., 8., 42., 1., 0., 0., 0.],\n",
- " [25., 8., 43., 1., 0., 0., 0.],\n",
- " [26., 8., 44., 1., 0., 0., 0.],\n",
- " [27., 8., 45., 1., 0., 0., 0.]], dtype=float32),\n",
- " array([[0., 0., 1., 0.]], dtype=float32))"
+ "((10, 7), (4,))"
]
},
- "execution_count": 37,
+ "execution_count": 63,
"metadata": {},
"output_type": "execute_result"
}
@@ -1090,6 +1130,36 @@
"sample_array[0][0][INDEX_TIMESTEP], sample_array[0][1][INDEX_TIMESTEP]"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### General Model Helper"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 64,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Adapted from https://www.tensorflow.org/tutorials/structured_data/time_series#linear_model\n",
+ "def compile_and_fit(model, window: WindowGenerator, loss = tf.losses.MeanSquaredError(), optimizer = tf.optimizers.Adam(), metrics = tf.metrics.MeanAbsoluteError(), patience:int = 2, epochs: int = MAX_EPOCHS):\n",
+ " early_stopping = tf.keras.callbacks.EarlyStopping(\n",
+ " monitor='val_loss',\n",
+ " patience=patience,\n",
+ " mode='min'\n",
+ " )\n",
+ "\n",
+ " model.compile(\n",
+ " loss=loss,\n",
+ " optimizer=optimizer,\n",
+ " metrics=metrics,\n",
+ " )\n",
+ "\n",
+ " return model.fit(window.training_ds, epochs=epochs, validation_data=window.validation_ds, callbacks=[early_stopping])"
+ ]
+ },
{
"cell_type": "markdown",
"metadata": {},
@@ -1099,7 +1169,7 @@
},
{
"cell_type": "code",
- "execution_count": 215,
+ "execution_count": 65,
"metadata": {},
"outputs": [],
"source": [
@@ -1110,24 +1180,24 @@
},
{
"cell_type": "code",
- "execution_count": 278,
+ "execution_count": 66,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Model: \"sequential_12\"\n",
+ "Model: \"sequential_7\"\n",
"_________________________________________________________________\n",
" Layer (type) Output Shape Param # \n",
"=================================================================\n",
- " lstm_14 (LSTM) (None, 10, 16) 1600 \n",
+ " lstm_8 (LSTM) (None, 16) 1536 \n",
" \n",
- " dense_9 (Dense) (None, 10, 4) 68 \n",
+ " dense_7 (Dense) (None, 4) 68 \n",
" \n",
"=================================================================\n",
- "Total params: 1,668\n",
- "Trainable params: 1,668\n",
+ "Total params: 1,604\n",
+ "Trainable params: 1,604\n",
"Non-trainable params: 0\n",
"_________________________________________________________________\n",
"None\n"
@@ -1137,9 +1207,10 @@
"source": [
"# Model Definition\n",
"lstm_model = keras.Sequential()\n",
- "lstm_model.add(layers.Input(shape=(TIME_STEP_INPUT, 8)))\n",
- "lstm_model.add(layers.LSTM(LSTM_UNITS, stateful=False, return_sequences=True))\n",
+ "lstm_model.add(layers.Input(shape=(INPUT_TIME_STEP, INPUT_FEATURES_SIZE)))\n",
"# lstm_model.add(layers.LSTM(LSTM_UNITS, stateful=False, return_sequences=True))\n",
+ "# lstm_model.add(layers.LSTM(LSTM_UNITS, stateful=False, return_sequences=True))\n",
+ "lstm_model.add(layers.LSTM(LSTM_UNITS, stateful=False, return_sequences=False))\n",
"lstm_model.add(layers.Dense(SLEEP_STAGES))\n",
"lstm_model.build()\n",
"print(lstm_model.summary())"
@@ -1147,13 +1218,47 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 67,
"metadata": {},
"outputs": [],
"source": [
"# Model Training\n",
"lstm_loss = keras.losses.SparseCategoricalCrossentropy(from_logits=True)\n",
- "lstm_optm = keras.optimizers.Adam(learning_rate=LSTM_LEARNING_RATE)"
+ "lstm_optm = keras.optimizers.Adam(learning_rate=LSTM_LEARNING_RATE)\n",
+ "lstm_metrics = [tf.keras.metrics.SparseCategoricalCrossentropy(from_logits=True), tf.keras.metrics.Accuracy()]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 68,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 1/20\n"
+ ]
+ },
+ {
+ "ename": "",
+ "evalue": "",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[1;31mCanceled future for execute_request message before replies were done"
+ ]
+ },
+ {
+ "ename": "",
+ "evalue": "",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[1;31mThe Kernel crashed while executing code in the the current cell or a previous cell. Please review the code in the cell(s) to identify a possible cause of the failure. Click <a href='https://aka.ms/vscodeJupyterKernelCrash'>here</a> for more info. View Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
+ ]
+ }
+ ],
+ "source": [
+ "lstm_history = compile_and_fit(model=lstm_model, window=wg, loss= lstm_loss, optimizer= lstm_optm, metrics=lstm_metrics)"
]
},
{
--
GitLab