334 lines
11 KiB
Python
334 lines
11 KiB
Python
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from contextlib import closing
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import html
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from io import StringIO
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import pytest
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from sklearn import config_context
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from sklearn.linear_model import LogisticRegression
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from sklearn.neural_network import MLPClassifier
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from sklearn.impute import SimpleImputer
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from sklearn.decomposition import PCA
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from sklearn.decomposition import TruncatedSVD
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from sklearn.pipeline import Pipeline
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from sklearn.pipeline import FeatureUnion
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from sklearn.compose import ColumnTransformer
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from sklearn.ensemble import VotingClassifier
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from sklearn.feature_selection import SelectPercentile
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from sklearn.cluster import Birch
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from sklearn.cluster import AgglomerativeClustering
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from sklearn.preprocessing import OneHotEncoder
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from sklearn.preprocessing import StandardScaler
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from sklearn.svm import LinearSVC
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from sklearn.svm import LinearSVR
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from sklearn.tree import DecisionTreeClassifier
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from sklearn.multiclass import OneVsOneClassifier
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from sklearn.ensemble import StackingClassifier
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from sklearn.ensemble import StackingRegressor
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from sklearn.gaussian_process.kernels import ExpSineSquared
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from sklearn.kernel_ridge import KernelRidge
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from sklearn.model_selection import RandomizedSearchCV
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from sklearn.utils._estimator_html_repr import _write_label_html
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from sklearn.utils._estimator_html_repr import _get_visual_block
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from sklearn.utils._estimator_html_repr import estimator_html_repr
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@pytest.mark.parametrize("checked", [True, False])
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def test_write_label_html(checked):
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# Test checking logic and labeling
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name = "LogisticRegression"
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tool_tip = "hello-world"
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with closing(StringIO()) as out:
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_write_label_html(out, name, tool_tip, checked=checked)
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html_label = out.getvalue()
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assert "LogisticRegression</label>" in html_label
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assert html_label.startswith('<div class="sk-label-container">')
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assert "<pre>hello-world</pre>" in html_label
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if checked:
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assert "checked>" in html_label
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@pytest.mark.parametrize("est", ["passthrough", "drop", None])
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def test_get_visual_block_single_str_none(est):
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# Test estimators that are represented by strings
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est_html_info = _get_visual_block(est)
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assert est_html_info.kind == "single"
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assert est_html_info.estimators == est
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assert est_html_info.names == str(est)
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assert est_html_info.name_details == str(est)
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def test_get_visual_block_single_estimator():
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est = LogisticRegression(C=10.0)
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est_html_info = _get_visual_block(est)
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assert est_html_info.kind == "single"
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assert est_html_info.estimators == est
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assert est_html_info.names == est.__class__.__name__
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assert est_html_info.name_details == str(est)
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def test_get_visual_block_pipeline():
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pipe = Pipeline(
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[
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("imputer", SimpleImputer()),
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("do_nothing", "passthrough"),
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("do_nothing_more", None),
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("classifier", LogisticRegression()),
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]
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)
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est_html_info = _get_visual_block(pipe)
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assert est_html_info.kind == "serial"
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assert est_html_info.estimators == tuple(step[1] for step in pipe.steps)
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assert est_html_info.names == [
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"imputer: SimpleImputer",
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"do_nothing: passthrough",
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"do_nothing_more: passthrough",
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"classifier: LogisticRegression",
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]
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assert est_html_info.name_details == [str(est) for _, est in pipe.steps]
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def test_get_visual_block_feature_union():
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f_union = FeatureUnion([("pca", PCA()), ("svd", TruncatedSVD())])
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est_html_info = _get_visual_block(f_union)
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assert est_html_info.kind == "parallel"
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assert est_html_info.names == ("pca", "svd")
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assert est_html_info.estimators == tuple(
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trans[1] for trans in f_union.transformer_list
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)
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assert est_html_info.name_details == (None, None)
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def test_get_visual_block_voting():
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clf = VotingClassifier(
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[("log_reg", LogisticRegression()), ("mlp", MLPClassifier())]
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)
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est_html_info = _get_visual_block(clf)
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assert est_html_info.kind == "parallel"
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assert est_html_info.estimators == tuple(trans[1] for trans in clf.estimators)
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assert est_html_info.names == ("log_reg", "mlp")
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assert est_html_info.name_details == (None, None)
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def test_get_visual_block_column_transformer():
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ct = ColumnTransformer(
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[("pca", PCA(), ["num1", "num2"]), ("svd", TruncatedSVD, [0, 3])]
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)
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est_html_info = _get_visual_block(ct)
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assert est_html_info.kind == "parallel"
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assert est_html_info.estimators == tuple(trans[1] for trans in ct.transformers)
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assert est_html_info.names == ("pca", "svd")
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assert est_html_info.name_details == (["num1", "num2"], [0, 3])
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def test_estimator_html_repr_pipeline():
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num_trans = Pipeline(
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steps=[("pass", "passthrough"), ("imputer", SimpleImputer(strategy="median"))]
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)
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cat_trans = Pipeline(
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steps=[
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("imputer", SimpleImputer(strategy="constant", missing_values="empty")),
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("one-hot", OneHotEncoder(drop="first")),
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]
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)
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preprocess = ColumnTransformer(
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[
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("num", num_trans, ["a", "b", "c", "d", "e"]),
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("cat", cat_trans, [0, 1, 2, 3]),
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]
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)
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feat_u = FeatureUnion(
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[
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("pca", PCA(n_components=1)),
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(
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"tsvd",
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Pipeline(
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[
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("first", TruncatedSVD(n_components=3)),
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("select", SelectPercentile()),
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]
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),
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),
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]
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)
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clf = VotingClassifier(
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[
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("lr", LogisticRegression(solver="lbfgs", random_state=1)),
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("mlp", MLPClassifier(alpha=0.001)),
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]
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)
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pipe = Pipeline(
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[("preprocessor", preprocess), ("feat_u", feat_u), ("classifier", clf)]
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)
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html_output = estimator_html_repr(pipe)
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# top level estimators show estimator with changes
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assert html.escape(str(pipe)) in html_output
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for _, est in pipe.steps:
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assert (
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'<div class="sk-toggleable__content"><pre>' + html.escape(str(est))
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) in html_output
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# low level estimators do not show changes
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with config_context(print_changed_only=True):
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assert html.escape(str(num_trans["pass"])) in html_output
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assert "passthrough</label>" in html_output
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assert html.escape(str(num_trans["imputer"])) in html_output
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for _, _, cols in preprocess.transformers:
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assert f"<pre>{html.escape(str(cols))}</pre>" in html_output
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# feature union
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for name, _ in feat_u.transformer_list:
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assert f"<label>{html.escape(name)}</label>" in html_output
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pca = feat_u.transformer_list[0][1]
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assert f"<pre>{html.escape(str(pca))}</pre>" in html_output
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tsvd = feat_u.transformer_list[1][1]
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first = tsvd["first"]
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select = tsvd["select"]
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assert f"<pre>{html.escape(str(first))}</pre>" in html_output
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assert f"<pre>{html.escape(str(select))}</pre>" in html_output
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# voting classifier
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for name, est in clf.estimators:
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assert f"<label>{html.escape(name)}</label>" in html_output
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assert f"<pre>{html.escape(str(est))}</pre>" in html_output
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@pytest.mark.parametrize("final_estimator", [None, LinearSVC()])
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def test_stacking_classsifer(final_estimator):
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estimators = [
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("mlp", MLPClassifier(alpha=0.001)),
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("tree", DecisionTreeClassifier()),
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]
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clf = StackingClassifier(estimators=estimators, final_estimator=final_estimator)
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html_output = estimator_html_repr(clf)
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assert html.escape(str(clf)) in html_output
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# If final_estimator's default changes from LogisticRegression
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# this should be updated
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if final_estimator is None:
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assert "LogisticRegression(" in html_output
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else:
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assert final_estimator.__class__.__name__ in html_output
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@pytest.mark.parametrize("final_estimator", [None, LinearSVR()])
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def test_stacking_regressor(final_estimator):
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reg = StackingRegressor(
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estimators=[("svr", LinearSVR())], final_estimator=final_estimator
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)
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html_output = estimator_html_repr(reg)
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assert html.escape(str(reg.estimators[0][0])) in html_output
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assert "LinearSVR</label>" in html_output
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if final_estimator is None:
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assert "RidgeCV</label>" in html_output
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else:
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assert html.escape(final_estimator.__class__.__name__) in html_output
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def test_birch_duck_typing_meta():
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# Test duck typing meta estimators with Birch
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birch = Birch(n_clusters=AgglomerativeClustering(n_clusters=3))
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html_output = estimator_html_repr(birch)
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# inner estimators do not show changes
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with config_context(print_changed_only=True):
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assert f"<pre>{html.escape(str(birch.n_clusters))}" in html_output
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assert "AgglomerativeClustering</label>" in html_output
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# outer estimator contains all changes
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assert f"<pre>{html.escape(str(birch))}" in html_output
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def test_ovo_classifier_duck_typing_meta():
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# Test duck typing metaestimators with OVO
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ovo = OneVsOneClassifier(LinearSVC(penalty="l1"))
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html_output = estimator_html_repr(ovo)
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# inner estimators do not show changes
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with config_context(print_changed_only=True):
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assert f"<pre>{html.escape(str(ovo.estimator))}" in html_output
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assert "LinearSVC</label>" in html_output
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# outer estimator
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assert f"<pre>{html.escape(str(ovo))}" in html_output
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def test_duck_typing_nested_estimator():
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# Test duck typing metaestimators with random search
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kernel_ridge = KernelRidge(kernel=ExpSineSquared())
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param_distributions = {"alpha": [1, 2]}
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kernel_ridge_tuned = RandomizedSearchCV(
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kernel_ridge,
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param_distributions=param_distributions,
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)
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html_output = estimator_html_repr(kernel_ridge_tuned)
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assert "estimator: KernelRidge</label>" in html_output
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@pytest.mark.parametrize("print_changed_only", [True, False])
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def test_one_estimator_print_change_only(print_changed_only):
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pca = PCA(n_components=10)
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with config_context(print_changed_only=print_changed_only):
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pca_repr = html.escape(str(pca))
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html_output = estimator_html_repr(pca)
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assert pca_repr in html_output
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def test_fallback_exists():
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"""Check that repr fallback is in the HTML."""
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pca = PCA(n_components=10)
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html_output = estimator_html_repr(pca)
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assert (
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f'<div class="sk-text-repr-fallback"><pre>{html.escape(str(pca))}'
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in html_output
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)
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def test_show_arrow_pipeline():
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"""Show arrow in pipeline for top level in pipeline"""
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pipe = Pipeline([("scale", StandardScaler()), ("log_Reg", LogisticRegression())])
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html_output = estimator_html_repr(pipe)
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assert (
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'class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline'
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in html_output
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)
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def test_invalid_parameters_in_stacking():
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"""Invalidate stacking configuration uses default repr.
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Non-regression test for #24009.
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"""
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stacker = StackingClassifier(estimators=[])
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html_output = estimator_html_repr(stacker)
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assert html.escape(str(stacker)) in html_output
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def test_estimator_get_params_return_cls():
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"""Check HTML repr works where a value in get_params is a class."""
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class MyEstimator:
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def get_params(self, deep=False):
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return {"inner_cls": LogisticRegression}
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est = MyEstimator()
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assert "MyEstimator" in estimator_html_repr(est)
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