Specify output dtype for Normalize op in ETL example to match model e…

…xpectations (#523) * Specify output dtype for Normalize op in ETL to match model * Convert timestamps to np.float32 in Getting Started ETL Notebook * Convert normalized cols to np.float32
NVIDIA-Merlin · Nov 11, 2022 · 11be6b1 · 11be6b1
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diff --git a/examples/end-to-end-session-based/01-ETL-with-NVTabular.ipynb b/examples/end-to-end-session-based/01-ETL-with-NVTabular.ipynb
@@ -415,7 +415,7 @@
     "    \n",
     "recency_features = session_ts >> ItemRecency() \n",
     "# Apply standardization to this continuous feature\n",
-    "recency_features_norm = recency_features >> nvt.ops.LogOp() >> nvt.ops.Normalize() >> nvt.ops.Rename(name='product_recency_days_log_norm')\n",
+    "recency_features_norm = recency_features >> nvt.ops.LogOp() >> nvt.ops.Normalize(out_dtype=np.float32) >> nvt.ops.Rename(name='product_recency_days_log_norm')\n",
     "\n",
     "time_features = (\n",
     "    session_time +\n",

diff --git a/examples/getting-started-session-based/01-ETL-with-NVTabular.ipynb b/examples/getting-started-session-based/01-ETL-with-NVTabular.ipynb
@@ -113,8 +113,8 @@
     "\n",
     "# generate category mapping for each item-id\n",
     "df['category'] = pd.cut(df['item_id'], bins=334, labels=np.arange(1, 335)).astype(np.int32)\n",
-    "df['timestamp/age_days'] = np.random.uniform(0, 1, NUM_ROWS)\n",
-    "df['timestamp/weekday/sin']= np.random.uniform(0, 1, NUM_ROWS)\n",
+    "df['timestamp/age_days'] = np.random.uniform(0, 1, NUM_ROWS).astype(np.float32)\n",
+    "df['timestamp/weekday/sin']= np.random.uniform(0, 1, NUM_ROWS).astype(np.float32)\n",
     "\n",
     "# generate day mapping for each session \n",
     "map_day = dict(zip(df.session_id.unique(), np.random.randint(1, 10, size=(df.session_id.nunique()))))\n",

diff --git a/examples/tutorial/02-ETL-with-NVTabular.ipynb b/examples/tutorial/02-ETL-with-NVTabular.ipynb
@@ -504,7 +504,7 @@
    "outputs": [],
    "source": [
     "recency_features = ['event_time_ts'] >> ItemRecency() \n",
-    "recency_features_norm = recency_features >> nvt.ops.LogOp() >> nvt.ops.Normalize() >> nvt.ops.Rename(name='product_recency_days_log_norm')"
+    "recency_features_norm = recency_features >> nvt.ops.LogOp() >> nvt.ops.Normalize(out_dtype=np.float32) >> nvt.ops.Rename(name='product_recency_days_log_norm')"
    ]
   },
   {
@@ -536,7 +536,7 @@
    "outputs": [],
    "source": [
     "# Smoothing price long-tailed distribution and applying standardization\n",
-    "price_log = ['price'] >> nvt.ops.LogOp() >> nvt.ops.Normalize() >> nvt.ops.Rename(name='price_log_norm')"
+    "price_log = ['price'] >> nvt.ops.LogOp() >> nvt.ops.Normalize(out_dtype=np.float32) >> nvt.ops.Rename(name='price_log_norm')"
    ]
   },
   {