diff --git a/.gitignore b/.gitignore
index 8fa6d85bae3877def6513c6d9fe4a837c087aa96..2269427c1edc001edae94bdf7e9fce0e53fad2d8 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,2 +1,3 @@
*.pdf
-.data/
\ No newline at end of file
+.data/
+*.pyc
\ No newline at end of file
diff --git a/tf_model.ipynb b/tf_model.ipynb
index efcdfa79fba233262de74809a897831726664598..4404a8cf282f6d3e69e8e2a9782861a11f28b61f 100644
--- a/tf_model.ipynb
+++ b/tf_model.ipynb
@@ -7,9 +7,16 @@
"# Notebook for training and testing AI models"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Setup"
+ ]
+ },
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
@@ -23,7 +30,7 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 4,
"metadata": {},
"outputs": [
{
@@ -40,7 +47,7 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
@@ -51,7 +58,7 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
@@ -234,7 +241,7 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
@@ -244,7 +251,7 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
@@ -259,7 +266,7 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
@@ -272,7 +279,7 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
@@ -282,7 +289,7 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 11,
"metadata": {},
"outputs": [
{
@@ -318,7 +325,7 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 12,
"metadata": {},
"outputs": [
{
@@ -595,7 +602,7 @@
"[551042 rows x 13 columns]"
]
},
- "execution_count": 11,
+ "execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
@@ -606,7 +613,7 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
@@ -616,7 +623,7 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 14,
"metadata": {},
"outputs": [
{
@@ -660,6 +667,221 @@
"print(sleep_data.info())"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>sleep_id</th>\n",
+ " <th>minutes_since_begin</th>\n",
+ " <th>stage_start_hour</th>\n",
+ " <th>stage_start_minute</th>\n",
+ " <th>awake_probability</th>\n",
+ " <th>rem_probability</th>\n",
+ " <th>light_probability</th>\n",
+ " <th>deep_probability</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>8</td>\n",
+ " <td>18</td>\n",
+ " <td>1.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>8</td>\n",
+ " <td>19</td>\n",
+ " <td>1.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>0</td>\n",
+ " <td>2</td>\n",
+ " <td>8</td>\n",
+ " <td>20</td>\n",
+ " <td>1.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>0</td>\n",
+ " <td>3</td>\n",
+ " <td>8</td>\n",
+ " <td>21</td>\n",
+ " <td>1.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>0</td>\n",
+ " <td>4</td>\n",
+ " <td>8</td>\n",
+ " <td>22</td>\n",
+ " <td>1.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>...</th>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>551037</th>\n",
+ " <td>1132</td>\n",
+ " <td>426</td>\n",
+ " <td>13</td>\n",
+ " <td>17</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>0.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>551038</th>\n",
+ " <td>1132</td>\n",
+ " <td>427</td>\n",
+ " <td>13</td>\n",
+ " <td>18</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>0.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>551039</th>\n",
+ " <td>1132</td>\n",
+ " <td>428</td>\n",
+ " <td>13</td>\n",
+ " <td>19</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>0.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>551040</th>\n",
+ " <td>1132</td>\n",
+ " <td>429</td>\n",
+ " <td>13</td>\n",
+ " <td>20</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>0.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>551041</th>\n",
+ " <td>1132</td>\n",
+ " <td>430</td>\n",
+ " <td>13</td>\n",
+ " <td>21</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>0.0</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>551042 rows × 8 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",
+ "\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",
+ "\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",
+ "\n",
+ "[551042 rows x 8 columns]"
+ ]
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "sleep_data"
+ ]
+ },
{
"cell_type": "markdown",
"metadata": {},
@@ -676,7 +898,7 @@
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
@@ -688,7 +910,7 @@
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
@@ -699,13 +921,13 @@
},
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": 31,
"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, generate_data_now: bool = True):\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",
" # 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",
@@ -723,7 +945,12 @@
" self.label_start = self.total_window_size - self.label_width\n",
" self.labels_slice = slice(self.label_start, None)\n",
" self.label_indices = np.arange(self.total_window_size)[self.labels_slice]\n",
- " \n",
+ "\n",
+ " self.input_feature_slice = input_feature_slice\n",
+ " self.label_feature_slice = label_feature_slice\n",
+ "\n",
+ " 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",
@@ -738,11 +965,10 @@
" ])\n",
"\n",
" def split_window(self, features):\n",
- " inputs = features[:, self.input_slice, :]\n",
- " labels = features[:, self.labels_slice, :]\n",
+ " inputs = features[:, self.input_slice, self.input_feature_slice]\n",
+ " labels = 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",
- "\n",
" return inputs, labels\n",
"\n",
" def make_dataset(self, data, index_group: str = \"sleep_id\", sort_by: str = \"minutes_since_begin\"):\n",
@@ -782,58 +1008,6 @@
" # return self._testing_ds"
]
},
- {
- "cell_type": "code",
- "execution_count": 17,
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "2022-05-11 00:38:01.423873: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\n",
- "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
- ]
- },
- {
- "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": 17,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "wg = WindowGenerator(sleep_data)\n",
- "wg"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 20,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "1836"
- ]
- },
- "execution_count": 20,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "len(wg.testing_ds)"
- ]
- },
{
"cell_type": "markdown",
"metadata": {},
@@ -845,92 +1019,75 @@
},
{
"cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [
- {
- "ename": "NameError",
- "evalue": "name 'training_test_split_by_unique_index' is not defined",
- "output_type": "error",
- "traceback": [
- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
- "\u001b[1;32m/Users/nowadmin/Documents/School Folder/CS 437/Lab/Final Project/tf_model.ipynb Cell 32'\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#ch0000068?line=0'>1</a>\u001b[0m training_sleep_data, test_sleep_data \u001b[39m=\u001b[39m training_test_split_by_unique_index(sleep_data, \u001b[39m\"\u001b[39m\u001b[39msleep_id\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m5\u001b[39m)\n",
- "\u001b[0;31mNameError\u001b[0m: name 'training_test_split_by_unique_index' is not defined"
- ]
- }
- ],
- "source": [
- "training_sleep_data, test_sleep_data = training_test_split_by_unique_index(sleep_data, \"sleep_id\", 5)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 288,
+ "execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "set()"
+ "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": 288,
+ "execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "set(training.sleep_id.unique()).intersection(set(test.sleep_id.unique()))"
+ "wg = WindowGenerator(sleep_data)\n",
+ "wg"
]
},
{
"cell_type": "code",
- "execution_count": 239,
+ "execution_count": 33,
"metadata": {},
"outputs": [],
"source": [
- "sleep_data_tensor = tf.convert_to_tensor(sleep_data)"
+ "sample = wg.sample_ds.take(1)"
]
},
{
"cell_type": "code",
- "execution_count": 240,
+ "execution_count": 34,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "TensorShape([551042, 7])"
- ]
- },
- "execution_count": 240,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
- "sleep_data_tensor.shape"
+ "sample_array = list(sample.as_numpy_iterator())"
]
},
{
"cell_type": "code",
- "execution_count": 241,
+ "execution_count": 37,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "<tf.Tensor: shape=(7,), dtype=float64, numpy=array([ 1., 8., 19., 1., 0., 0., 0.])>"
+ "(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))"
]
},
- "execution_count": 241,
+ "execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "sleep_data_tensor[1]"
+ "INDEX_TIMESTEP = 18\n",
+ "sample_array[0][0][INDEX_TIMESTEP], sample_array[0][1][INDEX_TIMESTEP]"
]
},
{