185 lines
3.3 KiB
Plaintext
185 lines
3.3 KiB
Plaintext
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!pip install scikit-learn
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!pip install matplotlib
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!pip install pandas
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!pip install numpy
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%matplotlib inline
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import pandas as pd
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import pylab as pl
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import numpy as np
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import scipy.optimize as opt
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from sklearn import preprocessing
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from sklearn.model_selection import train_test_split
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%matplotlib inline
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import matplotlib.pyplot as plt
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#Click here and press Shift+Enter
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!wget -O cell_samples.csv https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/Module%203/data/cell_samples.csv
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cell_df = pd.read_csv("cell_samples.csv")
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cell_df.head()
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ax = cell_df[cell_df['Class'] == 4][0:50].plot(kind='scatter', x='Clump', y='UnifSize', color='DarkBlue', label='malignant');
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cell_df[cell_df['Class'] == 2][0:50].plot(kind='scatter', x='Clump', y='UnifSize', color='Yellow', label='benign', ax=ax);
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plt.show()
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cell_df.dtypes
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cell_df = cell_df[pd.to_numeric(cell_df['BareNuc'], errors='coerce').notnull()]
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cell_df['BareNuc'] = cell_df['BareNuc'].astype('int')
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cell_df.dtypes
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feature_df = cell_df[['Clump', 'UnifSize', 'UnifShape', 'MargAdh', 'SingEpiSize', 'BareNuc', 'BlandChrom', 'NormNucl', 'Mit']]
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X = np.asarray(feature_df)
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X[0:5]
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y = np.asarray(cell_df['Class'])
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y [0:5]
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X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.2, random_state=4)
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print ('Train set:', X_train.shape, y_train.shape)
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print ('Test set:', X_test.shape, y_test.shape)
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from sklearn import svm
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clf = svm.SVC(kernel='rbf')
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clf.fit(X_train, y_train)
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yhat = clf.predict(X_test)
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yhat [0:5]
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from sklearn.metrics import classification_report, confusion_matrix
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import itertools
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def plot_confusion_matrix(cm, classes,
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normalize=False,
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title='Confusion matrix',
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cmap=plt.cm.Blues):
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"""
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This function prints and plots the confusion matrix.
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Normalization can be applied by setting `normalize=True`.
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"""
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if normalize:
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cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
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print("Normalized confusion matrix")
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else:
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print('Confusion matrix, without normalization')
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print(cm)
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plt.imshow(cm, interpolation='nearest', cmap=cmap)
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plt.title(title)
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plt.colorbar()
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tick_marks = np.arange(len(classes))
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plt.xticks(tick_marks, classes, rotation=45)
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plt.yticks(tick_marks, classes)
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fmt = '.2f' if normalize else 'd'
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thresh = cm.max() / 2.
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for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
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plt.text(j, i, format(cm[i, j], fmt),
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horizontalalignment="center",
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color="white" if cm[i, j] > thresh else "black")
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plt.tight_layout()
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plt.ylabel('True label')
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plt.xlabel('Predicted label')
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# Compute confusion matrix
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cnf_matrix = confusion_matrix(y_test, yhat, labels=[2,4])
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np.set_printoptions(precision=2)
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print (classification_report(y_test, yhat))
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# Plot non-normalized confusion matrix
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plt.figure()
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plot_confusion_matrix(cnf_matrix, classes=['Benign(2)','Malignant(4)'],normalize= False, title='Confusion matrix')
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from sklearn.metrics import f1_score
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f1_score(y_test, yhat, average='weighted')
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from sklearn.metrics import jaccard_score
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jaccard_score(y_test, yhat,pos_label=2)
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# write your code here
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