import os
import numpy.random as rnd


from sklearn.datasets import load_iris
from sklearn.datasets import make_moons
from sklearn.tree import DecisionTreeClassifier
from sklearn.tree import export_graphviz
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
from sklearn.tree import DecisionTreeRegressor


iris = load_iris()
X = iris.data[:, 2:]
y = iris.target
tree_clf = DecisionTreeClassifier(max_depth=2)
tree_clf.fit(X, y)

def image_path(fig_id):
	return fig_id

export_graphviz(tree_clf, 
				out_file=image_path("iris_tree.dot"), 
				feature_names=iris.feature_names[2:],
				class_names=iris.target_names,
				rounded=True,
				filled=True)

def plot_decision_boundary(clf, X, y, axes=[0, 7.5, 0, 3], iris=True, legend=False, plot_traning=True):
	x1s = np.linspace(axes[0], axes[1], 100)
	x2s = np.linspace(axes[2], axes[3], 100)
	x1, x2 = np.meshgrid(x1s, x2s)
	X_new = np.c_[x1.ravel(), x2.ravel()]
	y_pred = clf.predict(X_new).reshape(x1.shape)
	custom_cmap = ListedColormap(['#fafab0', '#9898ff', '#a0faa0'])
	plt.contourf(x1, x2, y_pred, alpha=0.3, cmap=custom_cmap, linewidth=10)
	if not iris:
		custom_cmap2 = ListedColormap(['#7d7d58', '#4c4c7f', '#507d50'])
		plt.contour(x1, x2, y_pred, cmap=custom_cmap2, alpha=0.8)
	if plot_traning:
		plt.plot(X[:, 0][y==0], X[:, 1][y==0],"yo", label="Iris-Setosa")
		plt.plot(X[:, 0][y==1], X[:, 1][y==1], "bs", label="Iris-Versicolor")
		plt.plot(X[:, 0][y==2], X[:, 1][y==2], "g^", label="Iris-Virginica")
		plt.axis(axes)
	if iris:
		plt.xlabel("Petal length", fontsize=14)
		plt.ylabel("Petal width", fontsize=14)
	else:
		plt.xlabel(r"$x_1$", fontsize=18)										
		plt.ylabel(r"$x_2$", fontsize=18, rotation=0)
	if legend:
		plt.legend(loc="lower right", fontsize=14)

plt.figure(figsize=(8, 4))																																										
plot_decision_boundary(tree_clf, X, y)
plt.plot([2.45, 2.45], [0, 3], "k-", linewidth=2)
plt.plot([2.45, 7.5], [1.75, 1.75], "k--", linewidth=2)
plt.plot([4.95, 4.95], [0, 1.75], "k:", linewidth=2)
plt.plot([4.85, 4.85], [1.75, 3], "k:", linewidth=2)
plt.text(1.40, 1.0, "Depth=0", fontsize=15)
plt.text(3.2, 1.80, "Depth=1", fontsize=13)
plt.text(4.05, 0.5, "(Depth=2)", fontsize=11)

plt.show()

print(tree_clf.predict_proba([[5, 1.5]]))

print(tree_clf.predict([[5, 1.5]]))

Xm, ym = make_moons(n_samples=100, noise=0.25, random_state=53)

deep_tree_clf1 = DecisionTreeClassifier(random_state=42)
deep_tree_clf2 = DecisionTreeClassifier(min_samples_leaf=4, random_state=42)
deep_tree_clf1.fit(Xm, ym)
deep_tree_clf2.fit(Xm, ym)

plt.figure(figsize=(11, 4))
plt.subplot(121)
plot_decision_boundary(deep_tree_clf1, Xm, ym, axes=[-1.5, 2.5, -1, 1.5], iris=False)
plt.title("No restrictions", fontsize=16)
plt.subplot(122)
plot_decision_boundary(deep_tree_clf2, Xm, ym, axes=[-1.5, 2.5, -1, 1.5], iris=False)
plt.title("min_samples_leaf = {}".format(deep_tree_clf2.min_samples_leaf), fontsize=14)

plt.show()

rnd.seed(42)
m = 200
X = rnd.rand(m, 1)
y = 4 * (X - 0.5) ** 2
y = y + rnd.randn(m, 1) / 10

tree_reg1 = DecisionTreeRegressor(random_state=42, max_depth=2)
tree_reg2 = DecisionTreeRegressor(random_state=42, max_depth=3)
tree_reg1.fit(X, y)
tree_reg2.fit(X, y)


def plot_regression_predictions(tree_reg, X, y, axes=[0, 1, -0.2, 1], ylabel="$y$"):
	x1 = np.linspace(axes[0], axes[1], 500).reshape(-1, 1)
	y_pred = tree_reg.predict(x1)
	plt.xlabel("$x_1$", fontsize=18)
	if ylabel:
		plt.ylabel(ylabel, fontsize=18, rotation=0)
	plt.plot(X, y, "b.")
	plt.plot(x1, y_pred, "r.-", linewidth=2, label=r"$\hat{y}$")

plt.figure(figsize=(11, 4))
plt.subplot(121)
plot_regression_predictions(tree_reg1, X, y)
for split, style in ((0.1973, "k-"), (0.0917, "k--"), (0.7718, "k--")):
	plt.plot([split, split],[-0.2, 1], style, linewidth=2)

plt.text(0.21, 0.65, "Depth=0", fontsize=15)
plt.text(0.01, 0.2, "Depth=1", fontsize=13)
plt.text(0.65, 0.8, "Depth=1", fontsize=13)
plt.legend(loc="upper center", fontsize=18)
plt.title("max_depth=2", fontsize=14)


plt.subplot(122)
plot_regression_predictions(tree_reg2, X, y, ylabel=None)
for split, style in ((0.1973, "k-"), (0.0917, "k--"), (0.7718, "k--")):
	plt.plot([split, split], [-0.2, 1], style, linewidth=2)
for split in (0.0458, 0.1298, 0.2873, 0.9040):
	plt.plot([split, split], [-0.2, 1], "k:", linewidth=1)

plt.text(0.3, 0.5, "Depth=2", fontsize=13)
plt.title("max_depth=3", fontsize=14)

plt.show()


tree_reg1 = DecisionTreeRegressor(random_state=42)
tree_reg2 = DecisionTreeRegressor(random_state=42, min_samples_leaf=10)
tree_reg1.fit(X, y)
tree_reg2.fit(X, y)

x1 = np.linspace(0, 1, 500).reshape(-1, 1)
y_pred1 = tree_reg1.predict(x1)
y_pred2 = tree_reg2.predict(x1)

plt.figure(figsize=(11, 4))

plt.subplot(121)
plt.plot(X, y, "b.")
plt.plot(x1, y_pred1, "r.-", linewidth=2, label=r"$\hat{y}$")
plt.axis([0, 1, -0.2, 1.1])
plt.xlabel("$x_1$", fontsize=18)
plt.ylabel("$y$", fontsize=18, rotation=0)
plt.legend(loc="upper center", fontsize=18)
plt.title("No restrictions", fontsize=18)
																																																																																																																																																																																																																																																																																																																																																																																																																																																																																																																																																																																								
plt.subplot(122)
plt.plot(X, y, "b.")
plt.plot(x1, y_pred2, "r.-", linewidth=2, label=r"$\hat{y}$")
plt.axis([0, 1, -0.2, 1.1])
plt.xlabel("$x_1$", fontsize=18)
plt.title("min_samples_leaf={}".format(tree_reg2.min_samples_leaf), fontsize=14)

plt.show()

rnd.seed(6)
Xs = rnd.rand(100, 2) - 0.5
ys = (Xs[:, 0] > 0).astype(np.float32) * 2

angle = np.pi / 4
rotation_matrix = np.array([[np.cos(angle), -np.sin(angle)],
							[np.sin(angle), np.cos(angle)]])

Xsr = Xs.dot(rotation_matrix)

tree_clf_s = DecisionTreeClassifier(random_state=42)
tree_clf_s.fit(Xs, ys)
tree_clf_sr = DecisionTreeClassifier(random_state=42)
tree_clf_sr.fit(Xsr, ys)

plt.figure(figsize=(11, 4))
plt.subplot(121)
plot_decision_boundary(tree_clf_s, Xs, ys, axes=[-0.7, 0.7, -0.7, 0.7], iris=False)
plt.subplot(122)
plot_decision_boundary(tree_clf_sr, Xsr, ys, axes=[-0.7, 0.7, -0.7, 0.7], iris=False)

plt.show()













