From 3040b01a62da872195421bd210b8e0720f4a0d4b Mon Sep 17 00:00:00 2001
From: bloodstalker <thabogre@gmail.com>
Date: Sat, 1 Sep 2018 19:41:26 +0430
Subject: update

---
 marionette.py | 101 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 101 insertions(+)
 create mode 100755 marionette.py

diff --git a/marionette.py b/marionette.py
new file mode 100755
index 0000000..2222940
--- /dev/null
+++ b/marionette.py
@@ -0,0 +1,101 @@
+#!/usr/bin/python3
+
+import argparse
+import code
+import readline
+import signal
+import sys
+import pandas
+from pandas import scatter_matrix
+import matplotlib.pyplot as plt
+from sklearn import model_selection
+from sklearn.metrics import classification_report
+from sklearn.metrics import confusion_matrix
+from sklearn.metrics import accuracy_score
+from sklearn.linear_model import LogisticRegression
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
+from sklearn.naive_bayes import GaussianNB
+from sklearn.svm import SVC
+
+def SigHandler_SIGINT(signum, frame):
+    print()
+    sys.exit(0)
+
+class Argparser(object):
+    def __init__(self):
+        parser = argparse.ArgumentParser()
+        parser.add_argument("--string", type=str, help="string")
+        parser.add_argument("--bool", action="store_true", help="bool", default=False)
+        parser.add_argument("--dbg", action="store_true", help="debug", default=False)
+        self.args = parser.parse_args()
+
+# write code here
+def premain(argparser):
+    signal.signal(signal.SIGINT, SigHandler_SIGINT)
+    #here
+    url = "https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data"
+    names = ["sepal-length", "sepal-width", "petal-length", "petal-width", "class"]
+    dataset = pandas.read_csv(url, names=names)
+    print(dataset.shape)
+    print(dataset.head(20))
+    print(dataset.describe())
+    print(dataset.groupby("class").size())
+    #dataset.plot(kind="box", subplots=True, layout=(2,2), sharex=False, sharey=False)
+    #dataset.hist()
+    pandas.plotting.scatter_matrix(dataset)
+    plt.show()
+    array = dataset.values
+    X = array[:,0:4]
+    Y = array[:,4]
+    validation_size = 0.20
+    seed = 7
+    X_train, X_validation, Y_train, Y_validation = model_selection.train_test_split(X,Y,test_size=validation_size, random_state=seed)
+    scoring="accuracy"
+    models = []
+    models.append(("LR", LogisticRegression()))
+    models.append(("LDA", LinearDiscriminantAnalysis()))
+    models.append(("KNN", KNeighborsClassifier()))
+    models.append(("CART", DecisionTreeClassifier()))
+    models.append(("NB", GaussianNB()))
+    models.append(("SVM", SVC()))
+    results = []
+    names = []
+    for name, model in models:
+        kfold = model_selection.KFold(n_splits=10, random_state=seed)
+        cv_results = model_selection.cross_val_score(model, X_train, Y_train, cv=kfold, scoring=scoring)
+        results.append(cv_results)
+        names.append(name)
+        msg = "%s:%f(%f)" % (name, cv_results.mean(), cv_results.std())
+        print(msg)
+    fig = plt.figure()
+    fig.suptitle("algorithm comparison")
+    ax = fig.add_subplot(111)
+    plt.boxplot(results)
+    ax.set_xticklabels(names)
+    plt.show()
+
+    # knn
+    knn = KNeighborsClassifier()
+    knn.fit(X_train, Y_train)
+    predictions = knn.predict(X_validation)
+    print(accuracy_score(Y_validation, predictions))
+    print(confusion_matrix(Y_validation, predictions))
+    print(classification_report(Y_validation, predictions))
+
+def main():
+    argparser = Argparser()
+    if argparser.args.dbg:
+        try:
+            premain(argparser)
+        except:
+            variables = globals().copy()
+            variables.update(locals())
+            shell = code.InteractiveConsole(variables)
+            shell.interact(banner="DEBUG REPL")
+    else:
+        premain(argparser)
+
+if __name__ == "__main__":
+    main()
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