Add template with flash & titanic (#123)

* move sample
* titanic
* fix parse name

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

Author: Borda

.actions/assistant.py

@@ -622,15 +622,17 @@ def update_env_details(dir_path: str):
         req += meta.get("requirements", [])
         req = [r.strip() for r in req]
 
-        def _parse(pkg: str, keys: str = " <=>[]") -> str:
+        def _parse_package_name(pkg: str, keys: str = " <=>[]@", egg_name: str = "#egg=") -> str:
             """Parsing just the package name."""
+            if egg_name in pkg:
+                pkg = pkg[pkg.index(egg_name) + len(egg_name) :]
             if any(c in pkg for c in keys):
                 ix = min(pkg.index(c) for c in keys if c in pkg)
                 pkg = pkg[:ix]
             return pkg
 
-        require = {_parse(r) for r in req if r}
-        env = {_parse(p): p for p in freeze.freeze()}
+        require = {_parse_package_name(r) for r in req if r}
+        env = {_parse_package_name(p): p for p in freeze.freeze()}
         meta["environment"] = [env[r] for r in require]
         meta["published"] = datetime.now().isoformat()
 

sample-template/.meta.yml templates/simple/.meta.yml

@@ -11,9 +11,3 @@ requirements:
 accelerator:
   - CPU
   - GPU
-datasets:
-  web:
-    # starting with http is downloaded
-    - https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz
-  kaggle:
-    - titanic

sample-template/.thumb.png templates/simple/.thumb.png

@@ -0,0 +1,18 @@
+title: Solving Titanic dataset with Lightning Flash
+author: PL team
+created: 2021-10-15
+updated: 2021-12-10
+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
+  - seaborn
+accelerator:
+  - CPU
+  - GPU
+datasets:
+  kaggle:
+    - titanic

sample-template/template.py templates/simple/template.py

@@ -0,0 +1,101 @@
+import os
+
+import matplotlib.pyplot as plt
+import pandas as pd
+import seaborn as sns
+import torch
+from flash import Trainer
+from flash.tabular import TabularClassificationData, TabularClassifier
+
+# %% [markdown]
+# ## 1. Create the DataModule
+#
+# ### Variable & Definition
+#
+# - survival: Survival (0 = No, 1 = Yes)
+# - pclass: Ticket class (1 = 1st, 2 = 2nd, 3 = 3rd)
+# - sex: Sex
+# - Age: Age in years
+# - sibsp: number of siblings / spouses aboard the Titanic
+# - parch: number of parents / children aboard the Titanic
+# - ticket: Ticket number
+# - fare: Passenger fare
+# - cabin: Cabin number
+# - embarked: Port of Embarkation
+
+# %%
+data_path = os.environ.get("PATH_DATASETS", "_datasets")
+path_titanic = os.path.join(data_path, "titanic")
+csv_train = os.path.join(path_titanic, "train.csv")
+csv_test = os.path.join(path_titanic, "test.csv")
+
+df_train = pd.read_csv(csv_train)
+df_train["Survived"].hist(bins=2)
+
+# %%
+datamodule = TabularClassificationData.from_csv(
+    categorical_fields=["Sex", "Embarked", "Cabin"],
+    numerical_fields=["Fare", "Age", "Pclass", "SibSp", "Parch"],
+    target_fields="Survived",
+    train_file=csv_train,
+    val_split=0.1,
+    batch_size=8,
+)
+
+# %% [markdown]
+# ## 2. Build the task
+
+# %%
+model = TabularClassifier.from_data(
+    datamodule,
+    learning_rate=0.1,
+    optimizer="Adam",
+    n_a=8,
+    gamma=0.3,
+)
+
+# %% [markdown]
+# ## 3. Create the trainer and train the model
+
+# %%
+from pytorch_lightning.loggers import CSVLogger  # noqa: E402]
+
+logger = CSVLogger(save_dir="logs/")
+trainer = Trainer(
+    max_epochs=10,
+    gpus=torch.cuda.device_count(),
+    logger=logger,
+    accumulate_grad_batches=12,
+    gradient_clip_val=0.1,
+)
+
+# %%
+
+trainer.fit(model, datamodule=datamodule)
+
+# %%
+
+metrics = pd.read_csv(f"{trainer.logger.log_dir}/metrics.csv")
+metrics.set_index("step", inplace=True)
+del metrics["epoch"]
+sns.relplot(data=metrics, kind="line")
+plt.gca().set_ylim([0, 1.25])
+plt.gcf().set_size_inches(10, 5)
+
+# %% [markdown]
+# ## 4. Generate predictions from a CSV
+
+# %%
+df_test = pd.read_csv(csv_test)
+
+predictions = model.predict(csv_test)
+print(predictions[0])
+
+# %%
+import numpy as np  # noqa: E402]
+
+assert len(df_test) == len(predictions)
+
+df_test["Survived"] = np.argmax(predictions, axis=-1)
+df_test.set_index("PassengerId", inplace=True)
+df_test["Survived"].hist(bins=5)