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September 29, 2017 09:07
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| #!/usr/bin/env python3 | |
| import matplotlib | |
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| from matplotlib.colors import ListedColormap | |
| from sklearn import tree | |
| from sklearn.tree import DecisionTreeRegressor | |
| from sklearn.datasets import load_iris | |
| from sklearn.externals.six import StringIO | |
| from sklearn import tree | |
| import pydotplus | |
| import pandas as pd | |
| import numpy as np | |
| import seaborn as sns | |
| from matplotlib import pyplot | |
| from sklearn import neighbors, model_selection | |
| from sklearn.model_selection import train_test_split | |
| from pandas import plotting | |
| import matplotlib.pyplot as plt | |
| from sklearn import neighbors, model_selection, tree, ensemble | |
| import seaborn as sns | |
| from sklearn import linear_model | |
| from sklearn.model_selection import train_test_split | |
| from sklearn.linear_model import LinearRegression, LogisticRegression | |
| from sklearn.linear_model import Ridge | |
| from sklearn.svm import SVC | |
| import os, sys | |
| from sys import stderr | |
| import gpxpy | |
| import gpxpy.gpx | |
| from sklearn.neighbors import KNeighborsClassifier | |
| from sklearn.ensemble import RandomForestClassifier | |
| df = pd.read_csv('data.csv', header=None) | |
| # Pandas dataframe to numpy.ndarray | |
| X = df[[2,3]].values | |
| y = df[1].values | |
| X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) | |
| def KNF(): | |
| clf = KNeighborsClassifier(n_neighbors=3) | |
| clf.fit(X_train, y_train) | |
| return clf | |
| def RandomForest(): | |
| forest = RandomForestClassifier(n_estimators=10, random_state=5) | |
| forest = forest.fit(X_train, y_train) | |
| return forest | |
| def DecisionTree(): | |
| clf = tree.DecisionTreeClassifier() | |
| clf = clf.fit(X_train, y_train) | |
| dot_data = StringIO() | |
| tree.export_graphviz(clf, out_file=dot_data) | |
| graph = pydotplus.graph_from_dot_data(dot_data.getvalue()) | |
| graph.write_pdf("iris.pdf") | |
| return clf | |
| def doDecisionTreeRegressor(): | |
| y = df[0].values | |
| X = df[2].values | |
| regr_1 = DecisionTreeRegressor(max_depth=2) | |
| regr_2 = DecisionTreeRegressor(max_depth=5) | |
| X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) | |
| regr_1.fit(X_train, y_train) | |
| regr_2.fit(X_train, y_train) | |
| y_1 = regr_1.predict(X_test) | |
| y_2 = regr_2.predict(X_test) | |
| # Plot the results | |
| # plt.figure(figsize=(8,6)) | |
| plt.scatter(X_test, y_1, c="darkorange", label="data") | |
| plt.plot(X_test, y_test, color="cornflowerblue", label="max_depth=2", linewidth=2) | |
| # plt.plot(X_test, y_2, color="yellowgreen", label="max_depth=5", linewidth=2) | |
| plt.xlabel("data") | |
| plt.ylabel("target") | |
| plt.title("Decision Tree Regression") | |
| plt.legend() | |
| plt.show() | |
| def visualize(): | |
| # Create color maps | |
| cmap_light = ListedColormap(['#FFB0AA', '#FFE0AA', '#FFF4AA', '#F2FAA7', '#7AB793', '#748BA7']) | |
| cmap_bold = ListedColormap(['#550000', '#553D00', '#4B5300', '#004011', '#041F37', '#14073A']) | |
| h = .1 # step size in the mesh | |
| clf = neighbors.KNeighborsClassifier(6) | |
| clf.fit(X, y) | |
| x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1 | |
| y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1 | |
| xx, yy = np.meshgrid(np.arange(x_min, x_max, h), | |
| np.arange(y_min, y_max, h)) | |
| Z = clf.predict(np.c_[xx.ravel(), yy.ravel()]) | |
| # Put the result into a color plot | |
| Z = Z.reshape(xx.shape) | |
| plt.figure() | |
| plt.pcolormesh(xx, yy, Z, cmap=cmap_light) | |
| # Plot also the training points | |
| plt.scatter(X[:, 0], X[:, 1], c=y, cmap=cmap_bold, | |
| s=10) | |
| plt.xlim(xx.min(), xx.max()) | |
| plt.ylim(yy.min(), yy.max()) | |
| plt.show() | |
| def get_model_quality(model): | |
| print("Training set score: {:.3f}".format(model.score(X_train, y_train))) | |
| print("Test set score: {:.3f}".format(model.score(X_test, y_test))) | |
| def predict(file, model): | |
| filename, file_extension = os.path.splitext(file) | |
| if file_extension != '.gpx': | |
| stderr.write('Please enter a valid GPX file.') | |
| return | |
| gpx_file = open(file, 'r') | |
| gpx = gpxpy.parse(gpx_file) | |
| if len(gpx.tracks) < 1: | |
| return | |
| data = gpx.tracks[0 | |
| ].get_moving_data() | |
| if (data.moving_time == 0 or data.max_speed == 0): | |
| return | |
| average_speed = data.moving_distance / data.moving_time | |
| max_speed = data.max_speed | |
| print(model.predict([[average_speed, max_speed]])) | |
| def main(): | |
| if len(sys.argv) == 1: | |
| #get_model_quality(KNF()) | |
| #get_model_quality(RandomForest()) | |
| get_model_quality(DecisionTree()) | |
| #visualize() | |
| #doDecisionTreeRegressor() | |
| else: | |
| predict(sys.argv[1], KNF()) | |
| if __name__ == '__main__': | |
| main() |
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