[FIX formatting in docs (#342)

* fix formatting in docs * Update examples/40_advanced/example_resampling_strategy.py Update README.md, remove cat requirements.txt
automl · Dec 8, 2021 · be0ba3e · be0ba3e
1 parent 49e846e
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diff --git a/README.md b/README.md
@@ -56,7 +56,6 @@ git submodule update --init --recursive
 conda create -n auto-pytorch python=3.8
 conda activate auto-pytorch
 conda install swig
-cat requirements.txt | xargs -n 1 -L 1 pip install
 python setup.py install
 
 ```

diff --git a/examples/40_advanced/example_custom_configuration_space.py b/examples/40_advanced/example_custom_configuration_space.py
@@ -5,6 +5,7 @@
 
 The following example shows how adjust the configuration space of
 the search. Currently, there are two changes that can be made to the space:-
+
 1. Adjust individual hyperparameters in the pipeline
 2. Include or exclude components:
     a) include: Dictionary containing components to include. Key is the node
@@ -57,80 +58,78 @@ def get_search_space_updates():
     return updates
 
 
-if __name__ == '__main__':
-
-    ############################################################################
-    # Data Loading
-    # ============
-    X, y = sklearn.datasets.fetch_openml(data_id=40981, return_X_y=True, as_frame=True)
-    X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(
-        X,
-        y,
-        random_state=1,
-    )
-
-    ############################################################################
-    # Build and fit a classifier with include components
-    # ==================================================
-    api = TabularClassificationTask(
-        search_space_updates=get_search_space_updates(),
-        include_components={'network_backbone': ['MLPBackbone', 'ResNetBackbone'],
-                            'encoder': ['OneHotEncoder']}
-    )
-
-    ############################################################################
-    # Search for an ensemble of machine learning algorithms
-    # =====================================================
-    api.search(
-        X_train=X_train.copy(),
-        y_train=y_train.copy(),
-        X_test=X_test.copy(),
-        y_test=y_test.copy(),
-        optimize_metric='accuracy',
-        total_walltime_limit=150,
-        func_eval_time_limit_secs=30
-    )
-
-    ############################################################################
-    # Print the final ensemble performance
-    # ====================================
-    y_pred = api.predict(X_test)
-    score = api.score(y_pred, y_test)
-    print(score)
-    print(api.show_models())
-
-    # Print statistics from search
-    print(api.sprint_statistics())
-
-    ############################################################################
-    # Build and fit a classifier with exclude components
-    # ==================================================
-    api = TabularClassificationTask(
-        search_space_updates=get_search_space_updates(),
-        exclude_components={'network_backbone': ['MLPBackbone'],
-                            'encoder': ['OneHotEncoder']}
-    )
-
-    ############################################################################
-    # Search for an ensemble of machine learning algorithms
-    # =====================================================
-    api.search(
-        X_train=X_train,
-        y_train=y_train,
-        X_test=X_test.copy(),
-        y_test=y_test.copy(),
-        optimize_metric='accuracy',
-        total_walltime_limit=150,
-        func_eval_time_limit_secs=30
-    )
-
-    ############################################################################
-    # Print the final ensemble performance
-    # ====================================
-    y_pred = api.predict(X_test)
-    score = api.score(y_pred, y_test)
-    print(score)
-    print(api.show_models())
-
-    # Print statistics from search
-    print(api.sprint_statistics())
+############################################################################
+# Data Loading
+# ============
+X, y = sklearn.datasets.fetch_openml(data_id=40981, return_X_y=True, as_frame=True)
+X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(
+    X,
+    y,
+    random_state=1,
+)
+
+############################################################################
+# Build and fit a classifier with include components
+# ==================================================
+api = TabularClassificationTask(
+    search_space_updates=get_search_space_updates(),
+    include_components={'network_backbone': ['MLPBackbone', 'ResNetBackbone'],
+                        'encoder': ['OneHotEncoder']}
+)
+
+############################################################################
+# Search for an ensemble of machine learning algorithms
+# =====================================================
+api.search(
+    X_train=X_train.copy(),
+    y_train=y_train.copy(),
+    X_test=X_test.copy(),
+    y_test=y_test.copy(),
+    optimize_metric='accuracy',
+    total_walltime_limit=150,
+    func_eval_time_limit_secs=30
+)
+
+############################################################################
+# Print the final ensemble performance
+# ====================================
+y_pred = api.predict(X_test)
+score = api.score(y_pred, y_test)
+print(score)
+print(api.show_models())
+
+# Print statistics from search
+print(api.sprint_statistics())
+
+############################################################################
+# Build and fit a classifier with exclude components
+# ==================================================
+api = TabularClassificationTask(
+    search_space_updates=get_search_space_updates(),
+    exclude_components={'network_backbone': ['MLPBackbone'],
+                        'encoder': ['OneHotEncoder']}
+)
+
+############################################################################
+# Search for an ensemble of machine learning algorithms
+# =====================================================
+api.search(
+    X_train=X_train,
+    y_train=y_train,
+    X_test=X_test.copy(),
+    y_test=y_test.copy(),
+    optimize_metric='accuracy',
+    total_walltime_limit=150,
+    func_eval_time_limit_secs=30
+)
+
+############################################################################
+# Print the final ensemble performance
+# ====================================
+y_pred = api.predict(X_test)
+score = api.score(y_pred, y_test)
+print(score)
+print(api.show_models())
+
+# Print statistics from search
+print(api.sprint_statistics())
diff --git a/examples/40_advanced/example_parallel_n_jobs.py b/examples/40_advanced/example_parallel_n_jobs.py
@@ -1,10 +1,11 @@
 """
-======================
-Tabular Classification
-======================
+============================================
+Tabular Classification with n parallel jobs
+============================================
 
 The following example shows how to fit a sample classification model parallely on 2 cores
 with AutoPyTorch
+
 """
 import os
 import tempfile as tmp
@@ -60,9 +61,9 @@
     ############################################################################
     # Print the final ensemble performance
     # ====================================
-    print(api.run_history, api.trajectory)
     y_pred = api.predict(X_test)
     score = api.score(y_pred, y_test)
     print(score)
     # Print the final ensemble built by AutoPyTorch
-    print(api.show_models())
+    print(api.sprint_statistics())
+
diff --git a/examples/40_advanced/example_resampling_strategy.py b/examples/40_advanced/example_resampling_strategy.py
@@ -26,10 +26,13 @@
 from autoPyTorch.api.tabular_classification import TabularClassificationTask
 from autoPyTorch.datasets.resampling_strategy import CrossValTypes, HoldoutValTypes
 
+############################################################################
+# Default Resampling Strategy
+# ============================
 
 ############################################################################
 # Data Loading
-# ============
+# ------------
 X, y = sklearn.datasets.fetch_openml(data_id=40981, return_X_y=True, as_frame=True)
 X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(
     X,
@@ -39,7 +42,7 @@
 
 ############################################################################
 # Build and fit a classifier with default resampling strategy
-# ===========================================================
+# -----------------------------------------------------------
 api = TabularClassificationTask(
     # 'HoldoutValTypes.holdout_validation' with 'val_share': 0.33
     # is the default argument setting for TabularClassificationTask.
@@ -51,7 +54,7 @@
 
 ############################################################################
 # Search for an ensemble of machine learning algorithms
-# =====================================================
+# -----------------------------------------------------
 api.search(
     X_train=X_train,
     y_train=y_train,
@@ -64,7 +67,7 @@
 
 ############################################################################
 # Print the final ensemble performance
-# ====================================
+# ------------------------------------
 y_pred = api.predict(X_test)
 score = api.score(y_pred, y_test)
 print(score)
@@ -76,17 +79,22 @@
 
 ############################################################################
 
+############################################################################
+# Cross validation Resampling Strategy
+# =====================================
+
 ############################################################################
 # Build and fit a classifier with Cross validation resampling strategy
-# ====================================================================
+# --------------------------------------------------------------------
 api = TabularClassificationTask(
     resampling_strategy=CrossValTypes.k_fold_cross_validation,
     resampling_strategy_args={'num_splits': 3}
 )
 
 ############################################################################
 # Search for an ensemble of machine learning algorithms
-# =====================================================
+# -----------------------------------------------------------------------
+
 api.search(
     X_train=X_train,
     y_train=y_train,
@@ -99,7 +107,7 @@
 
 ############################################################################
 # Print the final ensemble performance
-# ====================================
+# ------------
 y_pred = api.predict(X_test)
 score = api.score(y_pred, y_test)
 print(score)
@@ -111,9 +119,13 @@
 
 ############################################################################
 
+############################################################################
+# Stratified Resampling Strategy
+# ===============================
+
 ############################################################################
 # Build and fit a classifier with Stratified resampling strategy
-# ==============================================================
+# --------------------------------------------------------------
 api = TabularClassificationTask(
     # For demonstration purposes, we use
     # Stratified hold out validation. However,
@@ -124,7 +136,7 @@
 
 ############################################################################
 # Search for an ensemble of machine learning algorithms
-# =====================================================
+# -----------------------------------------------------
 api.search(
     X_train=X_train,
     y_train=y_train,

diff --git a/examples/40_advanced/example_run_with_portfolio.py b/examples/40_advanced/example_run_with_portfolio.py
@@ -24,50 +24,48 @@
 from autoPyTorch.api.tabular_classification import TabularClassificationTask
 
 
-if __name__ == '__main__':
+############################################################################
+# Data Loading
+# ============
+X, y = sklearn.datasets.fetch_openml(data_id=40981, return_X_y=True, as_frame=True)
+X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(
+    X,
+    y,
+    random_state=42,
+)
 
-    ############################################################################
-    # Data Loading
-    # ============
-    X, y = sklearn.datasets.fetch_openml(data_id=40981, return_X_y=True, as_frame=True)
-    X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(
-        X,
-        y,
-        random_state=42,
-    )
+############################################################################
+# Build and fit a classifier
+# ==========================
+api = TabularClassificationTask(
+    seed=42,
+)
 
-    ############################################################################
-    # Build and fit a classifier
-    # ==========================
-    api = TabularClassificationTask(
-        seed=42,
-    )
+############################################################################
+# Search for an ensemble of machine learning algorithms
+# =====================================================
+api.search(
+    X_train=X_train,
+    y_train=y_train,
+    X_test=X_test.copy(),
+    y_test=y_test.copy(),
+    optimize_metric='accuracy',
+    total_walltime_limit=300,
+    func_eval_time_limit_secs=50,
+    # Setting this option to "greedy"
+    # will make smac run the configurations
+    # present in 'autoPyTorch/configs/greedy_portfolio.json'
+    portfolio_selection="greedy"
+)
 
-    ############################################################################
-    # Search for an ensemble of machine learning algorithms
-    # =====================================================
-    api.search(
-        X_train=X_train,
-        y_train=y_train,
-        X_test=X_test.copy(),
-        y_test=y_test.copy(),
-        optimize_metric='accuracy',
-        total_walltime_limit=300,
-        func_eval_time_limit_secs=50,
-        # Setting this option to "greedy"
-        # will make smac run the configurations
-        # present in 'autoPyTorch/configs/greedy_portfolio.json'
-        portfolio_selection="greedy"
-    )
+############################################################################
+# Print the final ensemble performance
+# ====================================
+y_pred = api.predict(X_test)
+score = api.score(y_pred, y_test)
+print(score)
+# Print the final ensemble built by AutoPyTorch
+print(api.show_models())
 
-    ############################################################################
-    # Print the final ensemble performance
-    # ====================================
-    y_pred = api.predict(X_test)
-    score = api.score(y_pred, y_test)
-    print(score)
-    # Print the final ensemble built by AutoPyTorch
-    print(api.show_models())
-
-    # Print statistics from search
-    print(api.sprint_statistics())
+# Print statistics from search
+print(api.sprint_statistics())