CART Python add

CART/Python/CART.py

+import random
+import numpy as np
+
+
+def impurity(target):
+    acc = 0.0
+    unique, frequency = np.unique(target, return_counts=True)
+    size2 = float(target.shape[0])**2
+    for i, c in enumerate(unique):
+        acc += frequency[i]**2 / size2
+    return 1.0 - acc
+
+
+class CartTree:
+    def __init__(self, max_depth=4):
+        self.depth = 0  # start as root
+        self.left = None
+        self.right = None
+        self.feature = None
+        self.label = None
+        self.purity_gain = None
+        self.threshold = None
+        self.n_features = None  # to prune
+        self.max_depth = max_depth
+
+    def fit(self, features, target):
+        self.rec_tree_generation(features, target)
+        self.prune(self.max_depth)
+
+    def rec_tree_generation(self, features, target):
+        self.n_features = features.shape[0]
+        values, counts = np.unique(target, return_counts=True)
+        # A unique class -> found !
+        if len(counts) == 1:
+            self.label = target[0]
+            return
+        # Find the largest class
+        max_index = np.argmax(counts)
+        max_class = values[max_index]
+        self.label = max_class
+
+        best_gain = 0.0
+        best_feature = None
+        best_threshold = None
+        impurity_node = impurity(target)
+
+        # Select best feature to split along max_class
+        for feature_index in range(features.shape[1]):
+            feature_scale = np.unique(features[:, feature_index])
+            # print("Feature scale : ", feature_scale)
+            thresholds = (feature_scale[:-1] + feature_scale[1:]) / 2.0
+            # Thresholds are set to values between each features values
+            # print("Thresholds : ", thresholds)
+
+            for threshold in thresholds:
+                left_target = target[features[:, feature_index] <= threshold]
+                left_impurity = impurity(left_target)
+                n_left = float(left_target.shape[0]) / self.n_features
+
+                right_target = target[features[:, feature_index] > threshold]
+                right_impurity = impurity(right_target)
+                n_right = float(right_target.shape[0]) / self.n_features
+
+                # the larger purity gain, the better classification.
+                # (right and left) should be purer, so purity_gain > 0
+                purity_gain = impurity_node - (n_left * left_impurity + n_right * right_impurity)
+                if purity_gain > best_gain:
+                    best_gain = purity_gain
+                    best_feature = feature_index
+                    best_threshold = threshold
+
+        self.feature = best_feature
+        self.purity_gain = best_gain
+        self.threshold = best_threshold
+        print("Best Feature to split : ", self.feature, " Gain : ", self.purity_gain, " Threshold : ", self.threshold)
+
+        # Let the tree grow to the left and the right !
+        left_features = features[features[:, self.feature] <= self.threshold]  # select features
+        left_target = target[features[:, self.feature] <= self.threshold]  # select associated targets
+        self.left = CartTree()
+        self.left.depth = self.depth + 1
+        self.left.rec_tree_generation(left_features, left_target)
+
+        right_features = features[features[:, self.feature] > self.threshold]
+        right_target = target[features[:, self.feature] > self.threshold]
+        self.right = CartTree()
+        self.right.depth = self.depth + 1
+        self.right.rec_tree_generation(right_features, right_target)
+
+    def prune(self, max_depth):
+        if self.feature is None:
+            return
+
+        self.left.prune(max_depth)
+        self.right.prune(max_depth)
+
+        if self.depth >= max_depth:
+            self.left = None
+            self.right = None
+            self.feature = None
+
+    def predict(self, xfeatures):
+        return np.array([self._predict(xf) for xf in xfeatures])
+
+    def _predict(self, xfeature):
+        if self.feature != None:
+            if xfeature[self.feature] <= self.threshold:
+                return self.left._predict(xfeature)
+            else:
+                return self.right._predict(xfeature)
+        else:
+            return self.label

CART/Python/CropRec_main.py

+import pandas as pd
+from matplotlib import pyplot as plt
+from sklearn import tree
+from sklearn.metrics import ConfusionMatrixDisplay, confusion_matrix
+from sklearn.model_selection import train_test_split
+import graphviz
+from CART import *
+
+
+# Extract dataset
+dataset='croprec.csv'
+crop_df = pd.read_csv(dataset, header=0)
+Y = crop_df.label.values
+features = crop_df.columns.values[:-1]
+crop_df = crop_df.drop('label', axis=1)
+X = crop_df.values
+classes = np.unique(Y)
+
+print(features, classes)
+print(classes.shape)
+
+# TRAIN/TEST SPLIT
+X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.33, random_state=42)
+print("Splitting test, train", X_train.shape, X_test.shape)
+
+# My CART_TREES TREE
+print("Training !")
+myclassifier = CartTree(max_depth=12);
+myclassifier.fit(X_train, Y_train)
+print(myclassifier)
+
+# SKLEARN CART_TREES TREE
+clf = tree.DecisionTreeClassifier(random_state=None, max_depth=12)
+clf = clf.fit(X_train, Y_train)
+
+dot_data = tree.export_graphviz(clf,
+                                out_file=None,
+                                feature_names=features,
+                                class_names=classes,
+                                filled=True,
+                                rounded=True,
+                                special_characters=True)
+graph = graphviz.Source(dot_data)
+graph.render("Crop recommandations")
+
+# PREDICTIONS
+Y_pred = myclassifier.predict(X_test)
+print(confusion_matrix(Y_test, Y_pred, labels=classes))
+ConfusionMatrixDisplay.from_predictions(Y_test, Y_pred, display_labels=classes)
+plt.title("My predictions")
+plt.show()
+
+Y_pred = clf.predict(X_test)
+ConfusionMatrixDisplay.from_predictions(Y_test, Y_pred, display_labels=classes)
+plt.title("SKLEARN")
+plt.show()
+

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