Morten Hjorth-Jensen
78 lines
3.9 KiB
Plaintext
78 lines
3.9 KiB
Plaintext
Traceback (most recent call last):
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File "/Users/mhjensen/opt/anaconda3/lib/python3.8/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/opt/anaconda3/lib/python3.8/site-packages/nbclient/client.py", line 1087, in execute
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return NotebookClient(nb=nb, resources=resources, km=km, **kwargs).execute()
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File "/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/nbclient/util.py", line 74, in wrapped
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return just_run(coro(*args, **kwargs))
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File "/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/nbclient/util.py", line 53, in just_run
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return loop.run_until_complete(coro)
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File "/Users/mhjensen/opt/anaconda3/lib/python3.8/asyncio/base_events.py", line 616, in run_until_complete
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return future.result()
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File "/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/nbclient/client.py", line 540, in async_execute
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await self.async_execute_cell(
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File "/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/nbclient/client.py", line 832, in async_execute_cell
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self._check_raise_for_error(cell, exec_reply)
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File "/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/nbclient/client.py", line 740, 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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import matplotlib.pyplot as plt
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import numpy as np
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from sklearn.model_selection import train_test_split
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from sklearn.datasets import load_breast_cancer
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from sklearn.linear_model import LogisticRegression
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# Load the data
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cancer = load_breast_cancer()
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X_train, X_test, y_train, y_test = train_test_split(cancer.data,cancer.target,random_state=0)
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print(X_train.shape)
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print(X_test.shape)
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# Logistic Regression
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logreg = LogisticRegression(solver='lbfgs')
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logreg.fit(X_train, y_train)
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print("Test set accuracy with Logistic Regression: {:.2f}".format(logreg.score(X_test,y_test)))
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#now scale the data
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from sklearn.preprocessing import StandardScaler
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scaler = StandardScaler()
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scaler.fit(X_train)
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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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# Logistic Regression
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logreg.fit(X_train_scaled, y_train)
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print("Test set accuracy Logistic Regression with scaled data: {:.2f}".format(logreg.score(X_test_scaled,y_test)))
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from sklearn.preprocessing import LabelEncoder
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from sklearn.model_selection import cross_validate
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#Cross validation
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accuracy = cross_validate(logreg,X_test_scaled,y_test,cv=10)['test_score']
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print(accuracy)
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print("Test set accuracy with Logistic Regression and scaled data: {:.2f}".format(logreg.score(X_test_scaled,y_test)))
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import scikitplot as skplt
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y_pred = logreg.predict(X_test_scaled)
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skplt.metrics.plot_confusion_matrix(y_test, y_pred, normalize=True)
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plt.show()
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y_probas = logreg.predict_proba(X_test_scaled)
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skplt.metrics.plot_roc(y_test, y_probas)
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plt.show()
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skplt.metrics.plot_cumulative_gain(y_test, y_probas)
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plt.show()
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------------------
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[0;31m---------------------------------------------------------------------------[0m
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[0;31mModuleNotFoundError[0m Traceback (most recent call last)
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[0;32m<ipython-input-8-12adb44b1c20>[0m in [0;36m<module>[0;34m[0m
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[1;32m 34[0m [0;34m[0m[0m
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[1;32m 35[0m [0;34m[0m[0m
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[0;32m---> 36[0;31m [0;32mimport[0m [0mscikitplot[0m [0;32mas[0m [0mskplt[0m[0;34m[0m[0;34m[0m[0m
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[0m[1;32m 37[0m [0my_pred[0m [0;34m=[0m [0mlogreg[0m[0;34m.[0m[0mpredict[0m[0;34m([0m[0mX_test_scaled[0m[0;34m)[0m[0;34m[0m[0;34m[0m[0m
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[1;32m 38[0m [0mskplt[0m[0;34m.[0m[0mmetrics[0m[0;34m.[0m[0mplot_confusion_matrix[0m[0;34m([0m[0my_test[0m[0;34m,[0m [0my_pred[0m[0;34m,[0m [0mnormalize[0m[0;34m=[0m[0;32mTrue[0m[0;34m)[0m[0;34m[0m[0;34m[0m[0m
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[0;31mModuleNotFoundError[0m: No module named 'scikitplot'
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ModuleNotFoundError: No module named 'scikitplot'
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