Python sklearn.externals.six.StringIO() Examples
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Example #1
Source File: predict_enriched_decision_tree.py From PIDGINv2 with MIT License | 7 votes |
def createTree(matrix,label): kmeans = KMeans(n_clusters=moa_clusters, random_state=0).fit(matrix) vector = map(int,kmeans.labels_) pc_10 = int(len(querymatrix1)*0.01) clf = tree.DecisionTreeClassifier(min_samples_split=min_sampsplit,min_samples_leaf=min_leafsplit,max_depth=max_d) clf.fit(matrix,vector) dot_data = StringIO() tree.export_graphviz(clf, out_file=dot_data, feature_names=label, class_names=map(str,list(set(sorted(kmeans.labels_)))), filled=True, rounded=True, special_characters=True, proportion=False, impurity=True) out_tree = dot_data.getvalue() out_tree = out_tree.replace('True','Inactive').replace('False','Active').replace(' ≤ 0.5', '').replace('class', 'Predicted MoA') graph = pydot.graph_from_dot_data(str(out_tree)) try: graph.write_jpg(output_name_tree) except AttributeError: graph = pydot.graph_from_dot_data(str(out_tree))[0] graph.write_jpg(output_name_tree) return #initializer for the pool
Example #2
Source File: guided_tree.py From fairtest with Apache License 2.0 | 6 votes |
def print_tree(tree, outfile, encoders): """ Print a tree to a file Parameters ---------- tree : the tree structure outfile : the output file encoders : the encoders used to encode categorical features """ import pydot dot_data = StringIO() export_graphviz(tree, encoders, filename=dot_data) graph = pydot.graph_from_dot_data(dot_data.getvalue()) graph.write_pdf(outfile)
Example #3
Source File: DTSklearn.py From AiLearning with GNU General Public License v3.0 | 6 votes |
def show_pdf(clf): ''' 可视化输出 把决策树结构写入文件: http://sklearn.lzjqsdd.com/modules/tree.html Mac报错: pydotplus.graphviz.InvocationException: GraphViz's executables not found 解决方案: sudo brew install graphviz 参考写入: http://www.jianshu.com/p/59b510bafb4d ''' # with open("testResult/tree.dot", 'w') as f: # from sklearn.externals.six import StringIO # tree.export_graphviz(clf, out_file=f) import pydotplus from sklearn.externals.six import StringIO dot_data = StringIO() tree.export_graphviz(clf, out_file=dot_data) graph = pydotplus.graph_from_dot_data(dot_data.getvalue()) graph.write_pdf("../../../output/3.DecisionTree/tree.pdf") # from IPython.display import Image # Image(graph.create_png())
Example #4
Source File: DTSklearn.py From AiLearning with GNU General Public License v3.0 | 6 votes |
def show_pdf(clf): ''' 可视化输出 把决策树结构写入文件: http://sklearn.lzjqsdd.com/modules/tree.html Mac报错: pydotplus.graphviz.InvocationException: GraphViz's executables not found 解决方案: sudo brew install graphviz 参考写入: http://www.jianshu.com/p/59b510bafb4d ''' # with open("testResult/tree.dot", 'w') as f: # from sklearn.externals.six import StringIO # tree.export_graphviz(clf, out_file=f) import pydotplus from sklearn.externals.six import StringIO dot_data = StringIO() tree.export_graphviz(clf, out_file=dot_data) graph = pydotplus.graph_from_dot_data(dot_data.getvalue()) graph.write_pdf("output/3.DecisionTree/tree.pdf") # from IPython.display import Image # Image(graph.create_png())
Example #5
Source File: test_online_lda.py From twitter-stock-recommendation with MIT License | 6 votes |
def check_verbosity(verbose, evaluate_every, expected_lines, expected_perplexities): n_components, X = _build_sparse_mtx() lda = LatentDirichletAllocation(n_components=n_components, max_iter=3, learning_method='batch', verbose=verbose, evaluate_every=evaluate_every, random_state=0) out = StringIO() old_out, sys.stdout = sys.stdout, out try: lda.fit(X) finally: sys.stdout = old_out n_lines = out.getvalue().count('\n') n_perplexity = out.getvalue().count('perplexity') assert_equal(expected_lines, n_lines) assert_equal(expected_perplexities, n_perplexity)
Example #6
Source File: visualize_tree.py From kaggle-tools with MIT License | 6 votes |
def visualize_tree(clf, feature_names, class_names, output_file, method='pdf'): dot_data = StringIO() tree.export_graphviz(clf, out_file=dot_data, feature_names=iris.feature_names, class_names=iris.target_names, filled=True, rounded=True, special_characters=True, impurity=False) graph = pydotplus.graph_from_dot_data(dot_data.getvalue()) if method == 'pdf': graph.write_pdf(output_file + ".pdf") elif method == 'inline': Image(graph.create_png()) return graph # An example using the iris dataset
Example #7
Source File: predict_enriched_two_libraries_decision_tree.py From PIDGINv2 with MIT License | 5 votes |
def createTree(matrix,label): vector = [1] * len(querymatrix1) + [0] * len(querymatrix2) ratio = float(len(vector)-sum(vector))/float(sum(vector)) sw = np.array([ratio if i == 1 else 1 for i in vector]) pc_10 = int(len(querymatrix1)*0.01) clf = tree.DecisionTreeClassifier(min_samples_split=min_sampsplit,min_samples_leaf=min_leafsplit,max_depth=max_d) clf.fit(matrix,vector) dot_data = StringIO() tree.export_graphviz(clf, out_file=dot_data, feature_names=label, class_names=['File2','File1'], filled=True, rounded=True, special_characters=True, proportion=False, impurity=True) out_tree = dot_data.getvalue() out_tree = out_tree.replace('True','Inactive').replace('False','Active').replace(' ≤ 0.5', '') graph = pydot.graph_from_dot_data(str(out_tree)) try: graph.write_jpg(output_name_tree) except AttributeError: graph = pydot.graph_from_dot_data(str(out_tree))[0] graph.write_jpg(output_name_tree) return #initializer for the pool
Example #8
Source File: learn.py From uta with Apache License 2.0 | 5 votes |
def write_pdf(clf, fn): dot_data = StringIO() tree.export_graphviz(clf, out_file=dot_data) graph = pydot.graph_from_dot_data(dot_data.getvalue()) graph.write_pdf(fn)
Example #9
Source File: test_export.py From twitter-stock-recommendation with MIT License | 5 votes |
def test_graphviz_errors(): # Check for errors of export_graphviz clf = DecisionTreeClassifier(max_depth=3, min_samples_split=2) # Check not-fitted decision tree error out = StringIO() assert_raises(NotFittedError, export_graphviz, clf, out) clf.fit(X, y) # Check if it errors when length of feature_names # mismatches with number of features message = ("Length of feature_names, " "1 does not match number of features, 2") assert_raise_message(ValueError, message, export_graphviz, clf, None, feature_names=["a"]) message = ("Length of feature_names, " "3 does not match number of features, 2") assert_raise_message(ValueError, message, export_graphviz, clf, None, feature_names=["a", "b", "c"]) # Check class_names error out = StringIO() assert_raises(IndexError, export_graphviz, clf, out, class_names=[]) # Check precision error out = StringIO() assert_raises_regex(ValueError, "should be greater or equal", export_graphviz, clf, out, precision=-1) assert_raises_regex(ValueError, "should be an integer", export_graphviz, clf, out, precision="1")
Example #10
Source File: test_export.py From twitter-stock-recommendation with MIT License | 5 votes |
def test_friedman_mse_in_graphviz(): clf = DecisionTreeRegressor(criterion="friedman_mse", random_state=0) clf.fit(X, y) dot_data = StringIO() export_graphviz(clf, out_file=dot_data) clf = GradientBoostingClassifier(n_estimators=2, random_state=0) clf.fit(X, y) for estimator in clf.estimators_: export_graphviz(estimator[0], out_file=dot_data) for finding in finditer("\[.*?samples.*?\]", dot_data.getvalue()): assert_in("friedman_mse", finding.group())