update vae to hydra config

Pipfile

@@ -9,7 +9,7 @@ verify_ssl = true
 name = "downloadpytorch"
 
 [packages]
-numpy = "*"
+numpy = "==1.26"
 pandas = "*"
 nbdev = "*"
 fastcore = "*"

config/config.yaml

@@ -5,3 +5,27 @@ defaults:
 base_dir: ..
 model_dir: ${base_dir}/models
 num_workers: 4
+device: cuda
+
+# dataset and dataloader params
+n_lag: 48
+n_signals: 1
+test_split: 0.2
+batch_sz: 8
+drop_last: true
+
+# VAE params
+latent_dim: 24
+num_hidden_units: 512
+kernel_size: 5 # kernel size would be (kernel_size, n_signals)
+stride: 2 # stride would be (stride, n_signals)
+# training
+lr: 1e-3
+epochs: 50
+wd: 1e-6
+# scheduler
+patience: 10
+factor: 0.9
+
+# LSTM params
+lstm: 1

nbs/10_concepts.ipynb

@@ -25,17 +25,18 @@
    "outputs": [],
    "source": [
     "# | export\n",
-    "import numpy as np"
+    "import numpy as np\n",
+    "from sklearn.utils import check_array"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
     "# | hide\n",
-    "from nbdev.showdoc import *"
+    "from nbdev.showdoc import *\n"
    ]
   },
   {
@@ -115,7 +116,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -131,15 +132,20 @@
     "    start_step = end_step - window_size\n",
     "    indices = np.asarray(range(0, len(x))) if indices is None else indices\n",
     "    if return_indices:\n",
-    "        return indices[start_step:end_step]\n",
+    "        window = indices[start_step:end_step]\n",
     "    else:\n",
-    "        return x[indices[start_step:end_step], :]\n",
-    "    # x of shape (num_features, feature_len)"
+    "        window = x[indices[start_step:end_step]]\n",
+    "\n",
+    "    # x of shape (num_features, feature_len)\n",
+    "    try:\n",
+    "        return check_array(window, ensure_2d=True)\n",
+    "    except ValueError:\n",
+    "        return window.reshape(-1, 1)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
@@ -148,7 +154,7 @@
        "(100, 1)"
       ]
      },
-     "execution_count": null,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -159,16 +165,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([90, 91, 92, 93, 94, 95, 96, 97, 98, 99])"
+       "array([[90],\n",
+       "       [91],\n",
+       "       [92],\n",
+       "       [93],\n",
+       "       [94],\n",
+       "       [95],\n",
+       "       [96],\n",
+       "       [97],\n",
+       "       [98],\n",
+       "       [99]])"
       ]
      },
-     "execution_count": null,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -190,7 +205,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
@@ -199,7 +214,7 @@
        "90"
       ]
      },
-     "execution_count": null,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -211,16 +226,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
+       "array([[0],\n",
+       "       [1],\n",
+       "       [2],\n",
+       "       [3],\n",
+       "       [4],\n",
+       "       [5],\n",
+       "       [6],\n",
+       "       [7],\n",
+       "       [8],\n",
+       "       [9]])"
       ]
      },
-     "execution_count": null,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -238,7 +262,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -254,22 +278,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "time series index starting at 0 and ending at 9 has label 1\n",
-      "time series index starting at 10 and ending at 19 has label 0\n",
-      "time series index starting at 20 and ending at 29 has label 0\n",
-      "time series index starting at 30 and ending at 39 has label 0\n",
-      "time series index starting at 40 and ending at 49 has label 0\n",
-      "time series index starting at 50 and ending at 59 has label 0\n",
-      "time series index starting at 60 and ending at 69 has label 0\n",
-      "time series index starting at 70 and ending at 79 has label 0\n",
-      "time series index starting at 80 and ending at 89 has label 0\n"
+      "time series index starting at [0] and ending at [9] has label 1\n",
+      "time series index starting at [10] and ending at [19] has label 0\n",
+      "time series index starting at [20] and ending at [29] has label 0\n",
+      "time series index starting at [30] and ending at [39] has label 0\n",
+      "time series index starting at [40] and ending at [49] has label 0\n",
+      "time series index starting at [50] and ending at [59] has label 0\n",
+      "time series index starting at [60] and ending at [69] has label 0\n",
+      "time series index starting at [70] and ending at [79] has label 0\n",
+      "time series index starting at [80] and ending at [89] has label 0\n"
      ]
     }
    ],
@@ -304,7 +328,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [],
    "source": [

ts_vae_lstm/_modidx.py

@@ -41,5 +41,6 @@
                                  'ts_vae_lstm.vae.VAE': ('vae.html#vae', 'ts_vae_lstm/vae.py'),
                                  'ts_vae_lstm.vae.VAE.__init__': ('vae.html#vae.__init__', 'ts_vae_lstm/vae.py'),
                                  'ts_vae_lstm.vae.VAE.forward': ('vae.html#vae.forward', 'ts_vae_lstm/vae.py'),
+                                 'ts_vae_lstm.vae.as_df': ('vae.html#as_df', 'ts_vae_lstm/vae.py'),
                                  'ts_vae_lstm.vae.evaluate_reconstruction': ('vae.html#evaluate_reconstruction', 'ts_vae_lstm/vae.py'),
                                  'ts_vae_lstm.vae.validate_epoch': ('vae.html#validate_epoch', 'ts_vae_lstm/vae.py')}}}

ts_vae_lstm/ad_complete.py

@@ -1,3 +1,5 @@
+"""Fill in a module description here"""
+
 # AUTOGENERATED! DO NOT EDIT! File to edit: ../nbs/03_ad_complete.ipynb.
 
 # %% auto 0

ts_vae_lstm/concepts.py

@@ -1,10 +1,13 @@
+"""Fill in a module description here"""
+
 # AUTOGENERATED! DO NOT EDIT! File to edit: ../nbs/10_concepts.ipynb.
 
 # %% auto 0
 __all__ = ['get_window']
 
 # %% ../nbs/10_concepts.ipynb 2
 import numpy as np
+from sklearn.utils import check_array
 
 # %% ../nbs/10_concepts.ipynb 8
 def get_window(x, window_size=10, end_step=100, indices=None, return_indices=True):
@@ -16,7 +19,12 @@ def get_window(x, window_size=10, end_step=100, indices=None, return_indices=Tru
     start_step = end_step - window_size
     indices = np.asarray(range(0, len(x))) if indices is None else indices
     if return_indices:
-        return indices[start_step:end_step]
+        window = indices[start_step:end_step]
     else:
-        return x[indices[start_step:end_step], :]
+        window = x[indices[start_step:end_step]]
+
     # x of shape (num_features, feature_len)
+    try:
+        return check_array(window, ensure_2d=True)
+    except ValueError:
+        return window.reshape(-1, 1)

ts_vae_lstm/lstm.py

@@ -1,3 +1,5 @@
+"""Fill in a module description here"""
+
 # AUTOGENERATED! DO NOT EDIT! File to edit: ../nbs/02_lstm.ipynb.
 
 # %% auto 0