59 lines
1.8 KiB
Text
59 lines
1.8 KiB
Text
$ git show ready4analysis
|
|
commit 0915b050✂SHA1
|
|
Author: Elena Piscopia <elena@example.net>
|
|
Date: Tue Jun 18 16:13:00 2019 +0000
|
|
|
|
add script for kNN classification and plotting
|
|
|
|
diff --git a/code/script.py b/code/script.py
|
|
new file mode 100644
|
|
index 0000000..2d9bf3a
|
|
--- /dev/null
|
|
+++ b/code/script.py
|
|
@@ -0,0 +1,46 @@
|
|
+
|
|
+import argparse
|
|
+import matplotlib
|
|
+matplotlib.use('Agg')
|
|
+
|
|
+import pandas as pd
|
|
+import seaborn as sns
|
|
+from sklearn import model_selection
|
|
+from sklearn.neighbors import KNeighborsClassifier
|
|
+from sklearn.metrics import classification_report
|
|
+
|
|
+parser = argparse.ArgumentParser(description="Analyze iris data")
|
|
+parser.add_argument('data', help="Input data (CSV) to process")
|
|
+parser.add_argument('output_figure', help="Output figure path")
|
|
+parser.add_argument('output_report', help="Output report path")
|
|
+args = parser.parse_args()
|
|
+
|
|
+# prepare the data as a pandas dataframe
|
|
+df = pd.read_csv(args.data)
|
|
+attributes = ["sepal_length", "sepal_width", "petal_length","petal_width", "class"]
|
|
+df.columns = attributes
|
|
+
|
|
+# create a pairplot to plot pairwise relationships in the dataset
|
|
+plot = sns.pairplot(df, hue='class', palette='muted')
|
|
+plot.savefig(args.output_figure)
|
|
+
|
|
+# perform a K-nearest-neighbours classification with scikit-learn
|
|
+# Step 1: split data in test and training dataset (20:80)
|
|
+array = df.values
|
|
+X = array[:,0:4]
|
|
+Y = array[:,4]
|
|
+test_size = 0.20
|
|
+seed = 7
|
|
+X_train, X_test, Y_train, Y_test = model_selection.train_test_split(
|
|
+ X, Y,
|
|
+ test_size=test_size,
|
|
+ random_state=seed)
|
|
+# Step 2: Fit the model and make predictions on the test dataset
|
|
+knn = KNeighborsClassifier()
|
|
+knn.fit(X_train, Y_train)
|
|
+predictions = knn.predict(X_test)
|
|
+
|
|
+# Step 3: Save the classification report
|
|
+report = classification_report(Y_test, predictions, output_dict=True)
|
|
+df_report = pd.DataFrame(report).transpose().to_csv(args.output_report)
|
|
+
|