42 lines
1.3 KiB
Python
42 lines
1.3 KiB
Python
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from sklearn.model_selection import train_test_split
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from sklearn.tree import DecisionTreeClassifier
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import pandas as pd
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import numpy as np
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pd.options.mode.chained_assignment = None
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FILE_PATH = "WindData.csv"
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REQUIRED_COLUMNS = ['TI1', 'V1']
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TARGET_COLUMN_1 = 'TurbulenceIntensityClassA'
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TARGET_COLUMN_2 = 'TurbulenceIntensityClassB'
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TARGET_COLUMN_3 = 'TurbulenceIntensityClassC'
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def print_classifier_info(feature_importance):
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feature_names = REQUIRED_COLUMNS
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embarked_score = feature_importance[-3:].sum()
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scores = np.append(feature_importance[:2], embarked_score)
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scores = map(lambda score: round(score, 2), scores)
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print(dict(zip(feature_names, scores)))
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def actions(target_column):
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data = pd.read_csv(FILE_PATH)
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X = data[REQUIRED_COLUMNS]
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y = data[target_column]
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=100)
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classifier_tree = DecisionTreeClassifier(random_state=100)
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classifier_tree.fit(X_train, y_train)
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print_classifier_info(classifier_tree.feature_importances_)
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print("Оценка качества классификации ", target_column, " - ", classifier_tree.score(X_test, y_test))
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if __name__ == '__main__':
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actions(TARGET_COLUMN_1)
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actions(TARGET_COLUMN_2)
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actions(TARGET_COLUMN_3)
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