Rahul J lethalbrains
lethalbrains
/ decision_tree.py
Last active
August 14, 2018 19:31
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| import numpy as np | |
| import pandas as pd | |
| class DecisionTree: | |
| def __init__(self, max_depth = 6, depth = 1): | |
| self.max_depth = max_depth | |
| self.depth = depth | |
| self.left = None | |
| self.right = None |
lethalbrains
/ predict.py
Created
August 8, 2018 09:24
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| def predict(self, data): | |
| return np.array([self.__flow_data_thru_tree(row) for _, row in data.iterrows()]) | |
| def __flow_data_thru_tree(self, row): | |
| if self.is_leaf_node: return self.probability | |
| tree = self.left if row[self.split_feature] <= self.criteria else self.right | |
| return tree.__flow_data_thru_tree(row) |
lethalbrains
/ is_leaf_node.py
Created
August 8, 2018 09:21
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| @property | |
| def is_leaf_node(self): return self.left is None | |
| @property | |
| def probability(self): | |
| return self.data[self.target].value_counts().apply(lambda x: x/len(self.data)).tolist() |
lethalbrains
/ __depth.py
Created
August 8, 2018 08:31
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| def __init__(self, max_depth = 4, depth = 1): | |
| self.max_depth = max_depth | |
| self.depth = depth | |
| self.left = None | |
| self.right = None | |
| def __create_branches(self): | |
| self.left = DecisionTree(max_depth = self.max_depth, | |
| depth = self.depth + 1) | |
| self.right = DecisionTree(max_depth = self.max_depth, |
lethalbrains
/ __create_branches.py
Last active
August 8, 2018 07:49
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| def __create_branches(self): | |
| self.left = DecisionTree() | |
| self.right = DecisionTree() | |
| left_rows = self.data[self.data[self.split_feature] <= self.criteria] | |
| right_rows = self.data[self.data[self.split_feature] > self.criteria] | |
| self.left.fit(data = left_rows, target = self.target) | |
| self.right.fit(data = right_rows, target = self.target) |
lethalbrains
/ __find_best_split.py
Last active
August 8, 2018 07:32
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| def __find_best_split(self): | |
| best_split = {} | |
| for col in self.independent: | |
| information_gain, split = self.__find_best_split_for_column(col) | |
| if split is None: continue | |
| if not best_split or best_split["information_gain"] < information_gain: | |
| best_split = {"split": split, | |
| "col": col, | |
| "information_gain": information_gain} | |
| return best_split["split"], best_split["col"] |
lethalbrains
/ __find_best_split_for_column.py
Created
August 8, 2018 06:51
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| def __find_best_split_for_column(self, col): | |
| x = self.data[col] | |
| unique_values = x.unique() | |
| if len(unique_values) == 1: return None, None | |
| information_gain = None | |
| split = None | |
| for val in unique_values: | |
| left = x <= val | |
| right = x > val | |
| left_data = self.data[left] |
lethalbrains
/ __calculate_impurity_score.py
Last active
August 8, 2018 06:36
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| def __calculate_impurity_score(self, data): | |
| if data is None or data.empty: return 0 | |
| p_i, _ = data.value_counts().apply(lambda x: x/len(data)).tolist() | |
| return p_i * (1 - p_i) * 2 |
lethalbrains
/ crude_init_dtree.py
Created
August 7, 2018 20:57
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| def __init__(self): | |
| self.left = None | |
| self.right = None |
lethalbrains
/ decision_tree_crude_v1.py
Last active
August 8, 2018 06:05
Gist for "Learn ML Algorithms by coding" Blog
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| class DecisionTree: | |
| def fit(self, data, target): | |
| self.data = data | |
| self.target = target | |
| self.independent = self.data.columns.tolist() | |
| self.independent.remove(target) | |
| def predict(self, data): | |
| return np.array([self.__flow_data_thru_tree(row) for row in data.values]) |
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