CI: fix using Kaggle datasets

templates/titanic/.meta.yml

@@ -1,14 +1,16 @@
 title: Solving Titanic dataset with Lightning Flash
 author: PL team
 created: 2021-10-15
-updated: 2021-12-10
+updated: 2022-04-10
 license: CC
 build: 0
 description: |
   This is a template to show how to contribute a tutorial.
 requirements:
-  - https://github.com/PyTorchLightning/lightning-flash/archive/refs/tags/0.5.2.zip#egg=lightning-flash[tabular]
-  - matplotlib
+  - lightning-flash[tabular]>=0.7
+  - torchmetrics<0.8  # collision with `pytorch-tabular` which require PL 1.3.6
+  - pandas>=1.0
+  - matplotlib>=3.0
   - seaborn
 accelerator:
   - CPU

templates/titanic/tutorial.py

@@ -39,7 +39,7 @@
     target_fields="Survived",
     train_file=csv_train,
     val_split=0.1,
-    batch_size=8,
+    batch_size=32,
 )
 
 # %% [markdown]
@@ -49,7 +49,7 @@
 model = TabularClassifier.from_data(
     datamodule,
     learning_rate=0.1,
-    optimizer="Adam",
+    optimizer="AdamW",
     n_a=8,
     gamma=0.3,
 )
@@ -81,20 +81,29 @@
 sns.relplot(data=metrics, kind="line")
 plt.gca().set_ylim([0, 1.25])
 plt.gcf().set_size_inches(10, 5)
+plt.grid()
 
 # %% [markdown]
 # ## 4. Generate predictions from a CSV
 
 # %%
 df_test = pd.read_csv(csv_test)
 
-predictions = model.predict(csv_test)
-print(predictions[0])
+dm = TabularClassificationData.from_data_frame(
+    predict_data_frame=df_test,
+    parameters=datamodule.parameters,
+    batch_size=datamodule.batch_size,
+)
+preds = trainer.predict(model, datamodule=dm, output="classes")
+print(preds[0][:10])
 
 # %%
+import itertools  # noqa: E402]
+
 import numpy as np  # noqa: E402]
 
-assert len(df_test) == len(predictions)
+predictions = list(itertools.chain(*preds))
+# assert len(df_test) == len(predictions)
 
 df_test["Survived"] = np.argmax(predictions, axis=-1)
 df_test.set_index("PassengerId", inplace=True)