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+import argparse
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+import pandas as pd
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+import seaborn as sns
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+from sklearn import model_selection
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+from sklearn.neighbors import KNeighborsClassifier
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+from sklearn.metrics import classification_report
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+
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+parser = argparse.ArgumentParser(description="Analyze iris data")
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+parser.add_argument('data', help="Input data (CSV) to process")
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+parser.add_argument('output_figure', help="Output figure path")
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+parser.add_argument('output_report', help="Output report path")
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+args = parser.parse_args()
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+
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+# prepare the data as a pandas dataframe
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+df = pd.read_csv(args.data)
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+attributes = ["sepal_length", "sepal_width", "petal_length","petal_width", "class"]
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+df.columns = attributes
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+
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+# create a pairplot to plot pairwise relationships in the dataset
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+plot = sns.pairplot(df, hue='class', palette='muted')
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+plot.savefig(args.output_figure)
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+
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+# perform a K-nearest-neighbours classification with scikit-learn
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+
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+# Step 1: split data in test and training dataset (20:80)
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+array = df.values
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+X = array[:,0:4]
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+Y = array[:,4]
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+test_size = 0.20
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+seed = 7
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+X_train, X_test, Y_train, Y_test = model_selection.train_test_split(X, Y,
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+ test_size=test_size,
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+ random_state=seed)
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+
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+# Step 2: Fit the model and make predictions on the test dataset
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+knn = KNeighborsClassifier()
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+knn.fit(X_train, Y_train)
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+predictions = knn.predict(X_test)
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+
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+# Step 3: Save the classification report
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+report = classification_report(Y_test, predictions, output_dict=True)
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+df_report = pd.DataFrame(report).transpose().to_csv(args.output_report)
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