added basic regression example

machine-learning/scikit-learn_regression/scikit-learn_regression.py

+# %% [markdown]
+# # PPCES - Task: Regression with California Housing Dataset
+# In this task, participants should apply preprocessing and regression techniques to the California housing (real world) dataset from scikit-learn, which lists several houses, specific attributes of the houses and their prices. The task involves
+# - Loading the dataset
+# - Applying preprocessing techniques such as feature standardization
+# - Training and evaluating the regression model
+# - Visualization results and scores
+
+# %% [markdown]
+# ### Step 1: Load desired Python modules
+
+# %%
+import time
+import matplotlib.pyplot as plt
+
+from sklearn import datasets, preprocessing, metrics
+from sklearn.ensemble import RandomForestRegressor
+from sklearn.svm import SVR
+from sklearn.model_selection import train_test_split
+
+# %% [markdown]
+# ### Step 2: Loading the dataset
+# Load the dataset and print out the following information
+# - Description of the dataset
+# - Array shape of the feature data that is used for training the model
+# - Array shape of the label / target data
+# - List of the feature names
+# - List of the target names
+
+# %%
+# load the dataset
+dataset = datasets.fetch_california_housing()
+
+# print desired information
+print(f"Data set description:\n{dataset.DESCR}")
+print(f"Shape of feature / training data: {dataset.data.shape}")
+print(f"Shape of label data: {dataset.target.shape}")
+print(f"Feature names: {dataset.feature_names}")
+print(f"Target names: {dataset.target_names}")
+
+# %% [markdown]
+# ### Step 3: Data preprocessing
+# Run the following:
+# - Determine the min, max and avg values per feature
+#   - You should see that the value ranges are quite different per feature
+#   - Some models work better if all features have a similar order of magnitude
+# - Split the data into train and test splits
+# - Apply standardization to the training data split
+#   - Note: of course you also need to apply the same standardization to the test split later!
+
+# %%
+# determine min and max values per feature
+min_vals = list(dataset.data.min(axis=0))
+avg_vals = list(dataset.data.mean(axis=0))
+max_vals = list(dataset.data.max(axis=0))
+
+for i in range(len(min_vals)):
+    print(f"{dataset.feature_names[i]:15}:\tMin\t{min_vals[i]:8.3f}\tAvg\t{avg_vals[i]:8.3f}\tMax\t{max_vals[i]:8.3f}")
+
+# split data into train and test split (Use 20% test data)
+X_train, X_test, y_train, y_test = train_test_split(dataset.data, dataset.target, test_size=0.20, random_state=42, shuffle=True)
+
+# create a StandardScaler
+scaler = preprocessing.StandardScaler()
+
+# scale training data
+scaler = scaler.fit(X_train)
+X_train = scaler.transform(X_train)
+print(f"Scaler mean values (based on training set):\n{scaler.mean_}")
+print(f"Scaler scale values (based on training set):\n{scaler.scale_}")
+
+# dont forget to apply the same scaler to the test data split
+X_test = scaler.transform(X_test)
+
+# %% [markdown]
+# ### Step 4: Train a Support Vector Regression (SVR) or RandomForest Regression model
+# For SVR, use the following parameters:
+# - `C=1.0` (regularization parameter)
+# - `epsilon=0.2` (specifies the epsilon-tube within which no penalty is associated in the training loss function with points predicted within a distance epsilon from the actual value)
+# 
+# For RandomForest, use the following parameters:
+# - `n_estimators=10` (Number of different trees)
+# - `random_state=42`
+# - `max_depth=None` (depth of the trees, `None` will figure it out on its own)
+# - You can also play around with number of trees and depth
+
+# %%
+# create and intialize the model
+model = SVR(C=1.0, epsilon=0.2)
+# model = RandomForestRegressor(n_estimators=10, max_depth=None, random_state=42)
+
+# train / fit the model
+elapsed_time = time.time()
+model = model.fit(X_train, y_train)
+elapsed_time = time.time() - elapsed_time
+
+print(f"Elapsed time for training: {elapsed_time} sec")
+
+# %% [markdown]
+# ### Step 5: Evaluate the model using typical scoring functions
+# To evaluate the model performance, do the following:
+# - Predict the housing prices for the test split with the trained model (applying on unseen data)
+# - Plot both original (ground truth) and predicted values
+# - Determine `r2_score`, `mean_absolute_error` and `mean_absolute_percentage_error` for the prediction
+#   - Note: There are multiple scoring functions for different purposes. You can find more information here: https://scikit-learn.org/stable/modules/model_evaluation.html
+
+# %%
+# predict housing prices for test split
+y_pred = model.predict(X_test)
+
+# Plot both original (ground truth) and predicted values. Limited to the first 100 values to see differences 
+fig = plt.figure()
+plt.plot(y_test[:100]*100_000, color="blue", label="ground truth")
+plt.plot(y_pred[:100]*100_000, color="red", label="predicted")
+plt.legend()
+plt.xlabel("House")
+plt.ylabel("Price in USD")
+# plt.show()
+fig.savefig("plot_house_pricing.png", dpi=None, facecolor='w', edgecolor='w', 
+            format="png", transparent=False, bbox_inches='tight', pad_inches=0, metadata=None)
+
+# determine r2 and mean_absolute_error
+val_r2  = metrics.r2_score(y_test, y_pred)
+val_mae = metrics.mean_absolute_error(y_test, y_pred)
+val_mape = metrics.mean_absolute_percentage_error(y_test, y_pred)
+
+# print results
+print(f"R2: {val_r2}")
+print(f"MAE: {val_mae*100_000} whereas mean house prices are {y_test.mean()*100_000}")
+print(f"MAPE: {val_mape}")
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