Morten Hjorth-Jensen
69 lines
1.9 KiB
Python
69 lines
1.9 KiB
Python
# Common imports
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import numpy as np
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import pandas as pd
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import matplotlib.pyplot as plt
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from sklearn.tree import DecisionTreeClassifier
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from sklearn.model_selection import train_test_split
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from sklearn.tree import export_graphviz
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from sklearn.preprocessing import StandardScaler, OneHotEncoder
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from sklearn.compose import ColumnTransformer
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from IPython.display import Image
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from pydot import graph_from_dot_data
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import os
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# Where to save the figures and data files
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PROJECT_ROOT_DIR = "Results"
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FIGURE_ID = "Results/FigureFiles"
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DATA_ID = "DataFiles/"
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if not os.path.exists(PROJECT_ROOT_DIR):
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os.mkdir(PROJECT_ROOT_DIR)
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if not os.path.exists(FIGURE_ID):
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os.makedirs(FIGURE_ID)
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if not os.path.exists(DATA_ID):
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os.makedirs(DATA_ID)
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def image_path(fig_id):
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return os.path.join(FIGURE_ID, fig_id)
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def data_path(dat_id):
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return os.path.join(DATA_ID, dat_id)
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def save_fig(fig_id):
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plt.savefig(image_path(fig_id) + ".png", format='png')
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infile = open(data_path("grades.csv"),'r')
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# Read the experimental data with Pandas
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from IPython.display import display
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grades = pd.read_csv(infile,names = ('Trend','Sleep','Studied','Grade'))
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grades = pd.DataFrame(grades)
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# Features and targets
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X = grades.loc[:, grades.columns != 'Grade'].values
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y = grades.loc[:, grades.columns == 'Grade'].values
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# Create the encoder.
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encoder = OneHotEncoder(handle_unknown="ignore")
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# Assume for simplicity all features are categorical.
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encoder.fit(X)
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# Apply the encoder.
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X = encoder.transform(X)
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print(X)
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# Then do a Classification tree
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tree_clf = DecisionTreeClassifier(max_depth=2)
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tree_clf.fit(X, y)
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print("Train set accuracy with Decision Tree: {:.2f}".format(tree_clf.score(X,y)))
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#transfer to a decision tree graph
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export_graphviz(
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tree_clf,
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out_file="DataFiles/grade.dot",
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rounded=True,
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filled=True
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)
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cmd = 'dot -Tpng DataFiles/cancer.dot -o DataFiles/grades.png'
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os.system(cmd)
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