Morten Hjorth-Jensen
93 lines
3.9 KiB
Plaintext
93 lines
3.9 KiB
Plaintext
Traceback (most recent call last):
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File "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/jupyter_cache/executors/utils.py", line 51, in single_nb_execution
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executenb(
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File "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/nbclient/client.py", line 1204, in execute
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return NotebookClient(nb=nb, resources=resources, km=km, **kwargs).execute()
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File "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/nbclient/util.py", line 84, in wrapped
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return just_run(coro(*args, **kwargs))
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File "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/nbclient/util.py", line 62, in just_run
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return loop.run_until_complete(coro)
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File "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/asyncio/base_events.py", line 642, in run_until_complete
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return future.result()
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File "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/nbclient/client.py", line 663, in async_execute
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await self.async_execute_cell(
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File "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/nbclient/client.py", line 965, in async_execute_cell
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await self._check_raise_for_error(cell, cell_index, exec_reply)
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File "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/nbclient/client.py", line 862, in _check_raise_for_error
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raise CellExecutionError.from_cell_and_msg(cell, exec_reply_content)
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nbclient.exceptions.CellExecutionError: An error occurred while executing the following cell:
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------------------
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from sklearn import linear_model
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np.random.seed(2018)
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n = 10
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d = 2
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Lambda = 0.01
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# Make data set.
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x = np.linspace(-3, 3, n)
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y = 2.0 + 0.5*x + 5.0*(x**2)+ np.random.randn(n)
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# Design matrix X does not include the intercept.
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X = np.zeros((n, d))
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for p in range(d):
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X[:, p] = x ** (p+1)
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#Split data in train and test
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
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# Scale data by subtracting mean value of the input using scikit-learn
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scaler = StandardScaler(with_std=False)
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scaler.fit(X_train)
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X_train_mean = np.mean(X_train,axis=0)
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X_train_scaled = scaler.transform(X_train)
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X_test_scaled = scaler.transform(X_test)
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# We scale also the output, here by our own code
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y_scaler = np.mean(y_train)
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y_train_scaled = y_train - y_scaler
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y_test_scaled = y_test- y_scaler
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#Calculate beta
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OLS = LinearRegression()
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betaOLS=OLS.fit(X_train_scaled,y_train_scaled)
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ypredictOLS = OLS.predict(X_test_scaled)
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linear_model.Ridge(Lambda)
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RegRidge.fit(X_train_scaled,y_train_scaled)
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ypredictRidge = RegRidge.predict(X_test_scaled)
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betaOLS = OLS.coef_
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betaRidge = RegRidge.coef_
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print(betaOLS)
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print(betaRidge)
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interceptOLS = np.mean(y_train) - X_train_mean @ betaOLS
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interceptRidge = y_scaler - X_train_mean @ betaRidge
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print(interceptOLS)
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print(interceptRidge)
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#predict value
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ytilde_test_Ridge = X_test_scaled @ betaRidge+y_scaler
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ytilde_test_OLS = X_test_scaled @ betaOLS+y_scaler
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#Calculate MSE
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print(" ")
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print("test MSE of OLS")
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print(MSE(y_test,ytilde_test_OLS))
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print(" ")
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print("test MSE of Ridge")
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print(MSE(y_test,ytilde_test_Ridge))
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plt.scatter(x,y,label='Data')
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plt.plot(x, X @ RegRidge.coef_ + RegRidge.intercept_ , label="Ridge_Fit")
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plt.grid()
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plt.legend()
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plt.show()
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------------------
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[0;31m---------------------------------------------------------------------------[0m
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[0;31mNameError[0m Traceback (most recent call last)
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Input [0;32mIn [11][0m, in [0;36m<cell line: 34>[0;34m()[0m
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[1;32m 32[0m ypredictOLS [38;5;241m=[39m OLS[38;5;241m.[39mpredict(X_test_scaled)
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[1;32m 33[0m linear_model[38;5;241m.[39mRidge(Lambda)
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[0;32m---> 34[0m [43mRegRidge[49m[38;5;241m.[39mfit(X_train_scaled,y_train_scaled)
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[1;32m 35[0m ypredictRidge [38;5;241m=[39m RegRidge[38;5;241m.[39mpredict(X_test_scaled)
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[1;32m 36[0m betaOLS [38;5;241m=[39m OLS[38;5;241m.[39mcoef_
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[0;31mNameError[0m: name 'RegRidge' is not defined
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NameError: name 'RegRidge' is not defined
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