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        "author": "Majid Rafiei",
        "author_email": "majid.rafiei@pads.rwth-aachen.de",
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            "License :: OSI Approved :: MIT License",
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            "Programming Language :: Python :: 3"
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        "description": "# Visual Decision Tree Based on Categorical Attributes \n-------------------\n\nAs you may know \"scikit-learn\" library in python is not able to make a decision tree based on categorical data, and you have to convert categorical data to numerical before passing them to the classifier method. Also, the resulted decision tree is a binary tree while a decision tree does not need to be binary.\n\nHere, we provide a library which is able to make a visual decision tree based on categorical data. You can read more about decision trees [here](https://en.wikipedia.org/wiki/Decision_tree).\n\n## Features\n--------------------\n\nThe main algorithm which is used is ID3 with the following features:\n\n* Information gain based on [entropy](https://en.wikipedia.org/wiki/Decision_tree_learning)\n* Information gain based on [gini](https://en.wikipedia.org/wiki/Decision_tree_learning)\n* Some pruning capabilities like:\n\t* Minimum number of samples\n\t* Minimum information gain\n* The resulted tree is not binary\n\n## Requirements\n--------------------\n\nYou can find all the requirements in \"requirements.txt\" file, and it can be installed easily by the following command:\n\n* pip install -r requirements.txt \n\nAlso to be able to see visual tree, you need to install graphviz package. [Here](https://www.graphviz.org/download/) you can find the right package with respect to your operation system. \n\n\n## Usage\n--------------------\n\n```python\n\nfrom p_decision_tree.DecisionTree import DecisionTree\nimport pandas as pd\n\n#Reading CSV file as data set by Pandas\ndata = pd.read_csv('playtennis.csv')\ncolumns = data.columns\n\n#All columns except the last one are descriptive by default\ndescriptive_features = columns[:-1]\n#The last column is considered as label\nlabel = columns[-1]\n\n#Converting all the columns to string\nfor column in columns:\n    data[column]= data[column].astype(str)\n\ndata_descriptive = data[descriptive_features].values\ndata_label = data[label].values\n\n#Calling DecisionTree constructor (the last parameter is criterion which can also be \"gini\")\ndecisionTree = DecisionTree(data_descriptive.tolist(), descriptive_features.tolist(), data_label.tolist(), \"entropy\")\n\n#Here you can pass pruning features (gain_threshold and minimum_samples)\ndecisionTree.id3(0,0)\n\n#Visualizing decision tree by Graphviz\ndot = decisionTree.print_visualTree( render=True )\n\n# When using Jupyter\n#display( dot )\n\nprint(\"System entropy: \", format(decisionTree.entropy))\nprint(\"System gini: \", format(decisionTree.gini))\n\n\n\n``` \n\n\n\n",
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