Prześlij pliki do 'Sklearn'

Sklearn/Generate.py

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+# Load libraries
+import pickle
+import pandas as pd
+from sklearn import tree, metrics
+from sklearn.model_selection import train_test_split
+from sklearn.tree import DecisionTreeClassifier, _tree
+
+
+def tree_to_code(tree, feature_names):
+    # f = open('generatedTree.py', 'w')
+    tree_ = tree.tree_
+    feature_name = [
+        feature_names[i] if i != _tree.TREE_UNDEFINED else "undefined!"
+        for i in tree_.feature
+    ]
+    # print("def tree({}):".format(", ".join(feature_names)), file=f)
+    print("def tree({}):".format(", ".join(feature_names)))
+
+    def recurse(node, depth):
+        indent = "  " * depth
+        if tree_.feature[node] != _tree.TREE_UNDEFINED:
+            name = feature_name[node]
+            threshold = tree_.threshold[node]
+            # print("{}if {} <= {}:".format(indent, name, threshold), file=f)
+            print("{}if {} <= {}:".format(indent, name, threshold))
+            recurse(tree_.children_left[node], depth + 1)
+            # print("{}else:  # if {} > {}".format(indent, name, threshold), file=f)
+            print("{}else:  # if {} > {}".format(indent, name, threshold))
+            recurse(tree_.children_right[node], depth + 1)
+        else:
+            # print("{}return {}".format(indent, tree_.value[node],), file=f)
+            print("{}return {}".format(indent, tree_.value[node]))
+
+    recurse(0, 1)
+    # f.close()
+
+
+def loadLearningBase():
+    col_names = ['Warzywo', 'Nawoz', 'Srodek', 'Stan', 'Dzialanie']
+    base = pd.read_csv("Database.csv", header=None, names=col_names)
+    feature_cols = ['Warzywo', 'Nawoz', 'Srodek', 'Stan']
+
+    """ print dataset"""
+    # print(base.head())
+
+    X = base[feature_cols]  # Features
+    y = base.Dzialanie  # Target variable
+
+    # Split dataset into training set and test set
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3,
+                                                        random_state=1)  # 70% training and 30% test
+
+    data = generateDecisionTree(X_train, X_test, y_train, y_test)
+
+    """generate data for image"""
+    # tree.export_graphviz(data, out_file='treeData.dot', filled=True, rounded=True, special_characters=True,
+    #                      feature_names=feature_cols)
+
+    """Printing if_styled tree to console"""
+    # tree_to_code(data, feature_cols)
+
+    return data
+
+
+def generateDecisionTree(X_train, X_test, y_train, y_test):
+    # Create Decision Tree classifer object
+    clf = DecisionTreeClassifier(criterion="entropy")
+
+    # Train Decision Tree Classifer
+    clf = clf.fit(X_train, y_train)
+
+    # Predict the response for test dataset
+    y_pred = clf.predict(X_test)
+
+    """Model Accuracy, how often is the classifier correct """
+    # print("Accuracy:", metrics.accuracy_score(y_test, y_pred))
+
+    return clf
+
+
+if __name__ == '__main__':
+    generated = loadLearningBase()
+
+    # Save generated tree
+    filename = 'decisionTree.sav'
+    pickle.dump(generated, open(filename, 'wb'))

Sklearn/Test.py

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+import pickle
+
+
+def decision(prediction):
+    if prediction == 0:
+        return "Nie_podejmuj_działania"
+    elif prediction == 1:
+        return "Zastosuj_nawóz"
+    elif prediction == 2:
+        return "Zastosuj_środek"
+    elif prediction == 4:
+        return "Zbierz"
+    elif prediction == 5:
+        return "Roślina_już_zgniła-zbierz_i_wyrzuć"
+
+
+def test():
+    for n in range(0, 2):
+        if n == 0:
+            print("############# Nie ma nawozu #############")
+        else:
+            print("############# Zastosowano nawóz #############")
+        for s in range(0, 2):
+            if s == 0:
+                print("############# Nie ma środka ochrony #############")
+            else:
+                print("############# Zastosowano środek ochrony #############")
+            for st in range(0, 101):
+                val = tree.predict([[1, n, s, st]])
+                print("Stan roślinki: ", st, " ", decision(val))
+
+
+if __name__ == '__main__':
+    filename = 'decisionTree.sav'
+    tree = pickle.load(open(filename, 'rb'))
+    test()

Sklearn/injectCode.py

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+import pickle
+import sys
+
+
+def prediction(warzywo, nawoz ,srodek, stan_wzrostu):
+    filename = 'decisionTree.sav'
+    tree = pickle.load(open(filename, 'rb'))
+    print(tree.predict([[warzywo, nawoz, srodek, stan_wzrostu]]))
+
+
+
+if __name__ == '__main__':
+    # Map command line arguments to function arguments.
+    prediction(*sys.argv[1:])

Sklearn/treeData.dot

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+digraph Tree {
+node [shape=box, style="filled, rounded", color="black", fontname=helvetica] ;
+edge [fontname=helvetica] ;
+0 [label=<Stan &le; 40.5<br/>entropy = 2.092<br/>samples = 280<br/>value = [78, 41, 29, 108, 24]>, fillcolor="#e2e2fb"] ;
+1 [label=<Nawoz &le; 0.5<br/>entropy = 0.929<br/>samples = 119<br/>value = [78, 41, 0, 0, 0]>, fillcolor="#f3c3a1"] ;
+0 -> 1 [labeldistance=2.5, labelangle=45, headlabel="True"] ;
+2 [label=<Stan &le; 10.5<br/>entropy = 0.796<br/>samples = 54<br/>value = [13, 41, 0, 0, 0]>, fillcolor="#a5ed78"] ;
+1 -> 2 ;
+3 [label=<entropy = 0.0<br/>samples = 13<br/>value = [13, 0, 0, 0, 0]>, fillcolor="#e58139"] ;
+2 -> 3 ;
+4 [label=<entropy = 0.0<br/>samples = 41<br/>value = [0, 41, 0, 0, 0]>, fillcolor="#7be539"] ;
+2 -> 4 ;
+5 [label=<entropy = 0.0<br/>samples = 65<br/>value = [65, 0, 0, 0, 0]>, fillcolor="#e58139"] ;
+1 -> 5 ;
+6 [label=<Stan &le; 80.5<br/>entropy = 1.241<br/>samples = 161<br/>value = [0, 0, 29, 108, 24]>, fillcolor="#8a88ef"] ;
+0 -> 6 [labeldistance=2.5, labelangle=-45, headlabel="False"] ;
+7 [label=<entropy = 0.0<br/>samples = 108<br/>value = [0, 0, 0, 108, 0]>, fillcolor="#3c39e5"] ;
+6 -> 7 ;
+8 [label=<Srodek &le; 0.5<br/>entropy = 0.994<br/>samples = 53<br/>value = [0, 0, 29, 0, 24]>, fillcolor="#ddfbf5"] ;
+6 -> 8 ;
+9 [label=<entropy = 0.0<br/>samples = 29<br/>value = [0, 0, 29, 0, 0]>, fillcolor="#39e5c5"] ;
+8 -> 9 ;
+10 [label=<entropy = 0.0<br/>samples = 24<br/>value = [0, 0, 0, 0, 24]>, fillcolor="#e539c0"] ;
+8 -> 10 ;
+}