Add new examples

examples/20_basic/example_regression.py

@@ -11,18 +11,18 @@
 import sklearn.metrics
 
 import autosklearn.regression
+import matplotlib.pyplot as plt
 
-
-############################################################################
+############################
 # Data Loading
 # ============
 
-X, y = sklearn.datasets.load_boston(return_X_y=True)
+X, y = sklearn.datasets.load_diabetes(return_X_y=True)
 
 X_train, X_test, y_train, y_test = \
     sklearn.model_selection.train_test_split(X, y, random_state=1)
 
-############################################################################
+###########################
 # Build and fit a regressor
 # =========================
 
@@ -32,17 +32,43 @@
     tmp_folder='/tmp/autosklearn_regression_example_tmp',
     output_folder='/tmp/autosklearn_regression_example_out',
 )
-automl.fit(X_train, y_train, dataset_name='boston')
+automl.fit(X_train, y_train, dataset_name='diabetes')
 
-############################################################################
+######################################################
 # Print the final ensemble constructed by auto-sklearn
 # ====================================================
 
 print(automl.show_models())
 
-###########################################################################
+#####################################
 # Get the Score of the final ensemble
 # ===================================
+# After training the estimator, we can now quantify the goodness of fit. One possibility for
+# is the `R2 score <https://scikit-learn.org/stable/modules/model_evaluation.html#r2-score>`_.
+# The values range between -inf and 1 with 1 being the best possible value. A dummy estimator
+# predicting the data mean has an R2 score of 0.
+
+train_predictions = automl.predict(X_train)
+print("Train R2 score:", sklearn.metrics.r2_score(y_train, train_predictions))
+test_predictions = automl.predict(X_test)
+print("Test R2 score:", sklearn.metrics.r2_score(y_test, test_predictions))
+
+######################
+# Plot the predictions
+# ====================
+# Furthermore, we can now visually inspect the predictions. We plot the true value against the
+# predictions and show results on train and test data. Points on the diagonal depict perfect
+# predictions. Points below the diagonal were overestimated by the model (predicted value is higher
+# than the true value), points above the diagonal were underestimated (predicted value is lower than
+# the true value).
 
-predictions = automl.predict(X_test)
-print("R2 score:", sklearn.metrics.r2_score(y_test, predictions))
+plt.scatter(train_predictions, y_train, label="Train samples", c='#d95f02')
+plt.scatter(test_predictions, y_test, label="Test samples", c='#7570b3')
+plt.xlabel("Predicted value")
+plt.ylabel("True value")
+plt.legend()
+plt.plot([30, 400], [30, 400], c='k', zorder=0)
+plt.xlim([30, 400])
+plt.ylim([30, 400])
+plt.tight_layout()
+plt.show()

examples/40_advanced/example_inspect_predictions.py

@@ -0,0 +1,112 @@
+# -*- encoding: utf-8 -*-
+"""
+=================
+Model Explanation
+=================
+
+The following example shows how to fit a simple classification model with
+*auto-sklearn* and use the `inspect module <https://scikit-learn.org/stable/inspection.html>`_ from
+scikit-learn to understand what affects the predictions.
+"""
+import sklearn.datasets
+from sklearn.inspection import plot_partial_dependence, permutation_importance
+import matplotlib.pyplot as plt
+import autosklearn.classification
+
+
+#############################
+# Load Data and Build a Model
+# ===========================
+#
+# We start by loading the "Run or walk" dataset from OpenML and train an auto-sklearn model on it.
+# For this dataset, the goal is to predict whether a person is running or walking based on
+# accelerometer and gyroscope data collected by a phone. For more information see
+# `here <https://www.openml.org/d/40922>`_.
+
+dataset = sklearn.datasets.fetch_openml(data_id=40922)
+
+# Note: To speed up the example, we subsample the dataset
+dataset.data = dataset.data.sample(n=5000, random_state=1, axis="index")
+dataset.target = dataset.target[dataset.data.index]
+X_train, X_test, y_train, y_test = \
+    sklearn.model_selection.train_test_split(dataset.data, dataset.target, test_size=0.3,
+                                             random_state=1)
+
+automl = autosklearn.classification.AutoSklearnClassifier(
+    time_left_for_this_task=120,
+    per_run_time_limit=30,
+    tmp_folder='/tmp/autosklearn_classification_example_tmp',
+    output_folder='/tmp/autosklearn_classification_example_out',
+    n_jobs=1,
+)
+automl.fit(X_train, y_train, dataset_name='Run_or_walk_information')
+
+s = automl.score(X_train, y_train)
+print(f"Train score {s}")
+s = automl.score(X_test, y_test)
+print(f"Test score {s}")
+
+#########################################
+# Compute permutation importance - part 1
+# =======================================
+#
+# Since auto-sklearn implements the scikit-learn interface, it can be used with the scikit-learn's
+# inspection module. So, now we first look at the `permutation importance
+# <https://christophm.github.io/interpretable-ml-book/feature-importance.html>`_, which defines the
+# decrease in a model score when a given feature is randomly permuted. So, the higher the score,
+# the more does the model's predictions depend on this feature.
+#
+# **Note:** There are some pitfalls in interpreting these numbers, which can be found
+# in the `scikit-learn docs <https://scikit-learn.org/stable/modules/permutation_importance.html>`_.
+
+r = permutation_importance(automl, X_test, y_test,
+                           n_repeats=10,
+                           random_state=0)
+
+sort_idx = r.importances_mean.argsort()[::-1]
+plt.boxplot(r.importances[sort_idx].T, labels=[dataset.feature_names[i] for i in sort_idx])
+plt.xticks(rotation=90)
+plt.tight_layout()
+plt.show()
+
+for i in sort_idx[::-1]:
+    print(f"{dataset.feature_names[i]:10s}: {r.importances_mean[i]:.3f} +/- "
+          f"{r.importances_std[i]:.3f}")
+
+############################################################################################
+# Create partial dependence (PD) and individual conditional expectation (ICE) plots - part 2
+# ==========================================================================================
+#
+# `ICE <https://christophm.github.io/interpretable-ml-book/ice.html>`_ plots describe the relation
+# between feature values and the response value for each sample
+# individually -- it shows how the response value changes if the value of one feature is changed.
+#
+# `PD <https://christophm.github.io/interpretable-ml-book/pdp.html>`_ plots describe the relation
+# between feature values and the response value, i.e. the expected
+# response value wrt. one or multiple input features. Since we use a classification dataset, this
+# corresponds to the predicted class probability.
+#
+# Since ``acceleration_y`` and ``acceleration_z`` turned out to have the largest impact on the response
+# value according to the permutation dependence, we'll first look at them and generate a plot
+# combining ICE (thin lines) and PD (thick line)
+
+features = [1, 2]
+plot_partial_dependence(automl, dataset.data, features=features, grid_resolution=5,
+                        kind="both", feature_names=dataset.feature_names,
+                        )
+plt.tight_layout()
+plt.show()
+
+##########################################################################
+# Create partial dependence (PDP) plots for more than one feature - part 3
+# ========================================================================
+#
+# A PD plot can also be generated for two features and thus allow to inspect the interaction between
+# these features. Again, we'll look at acceleration_y and acceleration_z.
+
+features = [[1, 2]]
+plot_partial_dependence(automl, dataset.data, features=features, grid_resolution=5,
+                        feature_names=dataset.feature_names,
+                        )
+plt.tight_layout()
+plt.show()