From 56061fa6dc715b764bc4b1a145d9508908746c17 Mon Sep 17 00:00:00 2001 From: ana Date: Thu, 31 Mar 2022 16:37:59 +0200 Subject: [PATCH] Exploring and commenting script from scratch --- commenting_code_model/iris_data.csv | 150 ++++++++ commenting_code_model/sonar_csv.csv | 208 +++++++++++ .../testing_random_forest_model.py | 336 ++++++++++++++++++ 3 files changed, 694 insertions(+) create mode 100644 commenting_code_model/iris_data.csv create mode 100644 commenting_code_model/sonar_csv.csv create mode 100644 commenting_code_model/testing_random_forest_model.py diff --git a/commenting_code_model/iris_data.csv b/commenting_code_model/iris_data.csv new file mode 100644 index 0000000..90bf3a7 --- /dev/null +++ b/commenting_code_model/iris_data.csv @@ -0,0 +1,150 @@ +5.1,3.5,1.4,0.2,Iris Setosa +4.9,3,1.4,0.2,Iris Setosa +4.7,3.2,1.3,0.2,Iris Setosa +4.6,3.1,1.5,0.2,Iris Setosa +5,3.6,1.4,0.2,Iris Setosa +5.4,3.9,1.7,0.4,Iris Setosa +4.6,3.4,1.4,0.3,Iris Setosa +5,3.4,1.5,0.2,Iris Setosa 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/dev/null +++ b/commenting_code_model/testing_random_forest_model.py @@ -0,0 +1,336 @@ +# Using this tutorial : From scratch: https://machinelearningmastery.com/implement-random-forest-scratch-python/ +# Explanation on decision trees & random forest: https://pad.constantvzw.org/p/anais_berck_frart_Meise_random_forest + +# Random Forest Algorithm on Sonar Dataset +from random import seed +from random import randrange +from csv import reader +from math import sqrt +import json + +# Load a CSV file +def load_csv(filename): + dataset = list() + with open(filename, 'r') as file: + csv_reader = reader(file) + for row in csv_reader: + if not row: + continue + dataset.append(row) + return dataset + +# Convert string column to float - original dataset is in string format +def str_column_to_float(dataset, column): + for row in dataset: + row[column] = float(row[column].strip()) + +# Convert string column to integer / transforms classes 'mine' and 'rock' into 1 and 2 +def str_column_to_int(dataset, column): + # extracts values of the classes of the dataset: array of all the mine, rock + class_values = [row[column] for row in dataset] + # it transforms array into a set of 2 strings, mine and rock + unique = set(class_values) + # create dictionary + lookup = dict() + # loops through the set and creates dictionary with key (value: rock or mine) and the value is a number + # destruct tuple by using enumerate with 2 variables i, value - useful if you want to get indexes from a list + for i, value in enumerate(unique): + lookup[value] = i + # loops through rows of dataset, replaces the name of class by number/index value + for row in dataset: + row[column] = lookup[row[column]] + # code returns lookup table + return lookup + +# Split a dataset into k folds +def cross_validation_split(dataset, n_folds): + # creates list + dataset_split = list() + # copies the dataset (which is a list) using the list function + dataset_copy = list(dataset) + # size of the fold = length of dataset / amount of folds (ex. 5) + fold_size = int(len(dataset) / n_folds) + # loops through list of numbers from 0 up to 4 + for i in range(n_folds): + fold = list() + # as long as length of list is inferior to defined fold size + while len(fold) < fold_size: + # generates random integer between 0 and total length of dataset + index = randrange(len(dataset_copy)) + # pop removes element from list and returns the value, adds value to fold + fold.append(dataset_copy.pop(index)) + # adds fold to list of folds, each fold has different observations + dataset_split.append(fold) + print("dataset_split:", dataset_split) + return dataset_split + +# Calculate accuracy percentage +def accuracy_metric(actual, predicted): + correct = 0 + # loops through index list which has length of actual classes + for i in range(len(actual)): + # compares values on same positions in list of actual classes with list of predicted classes + if actual[i] == predicted[i]: + # adds one to correct variable to count number of correct guesses + correct += 1 + # gives percentage by dividing correct guesses by length of actual classes divided by 100 + return correct / float(len(actual)) * 100.0 + +# Evaluate an algorithm using a cross validation split +def evaluate_algorithm(dataset, algorithm, n_folds, *args): + # split dataset in n folds + folds = cross_validation_split(dataset, n_folds) + # creates list called scores + scores = list() + for fold in folds: + # creates copy of dataset_split, list of lists + train_set = list(folds) + # removes 1 fold for testing + train_set.remove(fold) + # concatenates all lists of remaining folds into 1 list + train_set = sum(train_set, []) + # creates a list called test_set + test_set = list() + # iterates through test data + for row in fold: + # makes copy of the row + row_copy = list(row) + # appends copy to a new list called test set + test_set.append(row_copy) + # changes last element in the row, which is the class, to remove the number and set it to None + row_copy[-1] = None + # he specifies randomforest as an algorithm, which is also a function; this line is + # generating the model based on trainset, makes prediction of classes for testset + predicted = algorithm(train_set, test_set, *args) + # list comprehension: gives list of actual classes in fold + actual = [row[-1] for row in fold] + # compares actual classes to predicted classes to give idea of accuracy of function + accuracy = accuracy_metric(actual, predicted) + # creates a list of scores based on comparison + scores.append(accuracy) + return scores + +# -------- + + +# Split a dataset based on a feature and a feature value defined in build tree +# just trying many times, benefitting from speed of computer +def test_split(index, value, dataset): + left, right = list(), list() + for row in dataset: + # compares set value to all values in that column, if it is smaller, it goes to the left + # he goes for each value through all dataset again + if row[index] < value: + left.append(row) + # comparing the set value to itself, then it goes to the right + else: + right.append(row) + return left, right + +# Calculate the Gini index for a split dataset, using left/right og test split as groups +# cfr calculating wealth distribution: https://en.wikipedia.org/wiki/Gini_coefficient +def gini_index(groups, classes): + # count all samples at split point (the dataset), converts it in a float in order to do divisions + n_instances = float(sum([len(group) for group in groups])) + # sum weighted Gini index for each group + gini = 0.0 + for group in groups: + size = float(len(group)) + # avoid divide by zero + if size == 0: + continue + score = 0.0 + # score the group based on the score for each class + # count number of instances for current class in the group and divide by total size of the group + for class_val in classes: + # outcome lies always between 0 and 1 + # for each row it takes the class value and counts how many times the set class value appears, divided by size of the group + p = [row[-1] for row in group].count(class_val) / size + # multiply makes it exponentially smaller; you amplify the badness of the score + score += p * p + # weight the group score by its relative size (size of group divided by total size of dataset) + gini += (1.0 - score) * (size / n_instances) + return gini + +# Select the best split point for a dataset +def get_split(dataset, n_features): + # takes last element of each row (class) and returns it as a row, as it is a set, it has only 2 values + class_values = list(set(row[-1] for row in dataset)) + # assigning values to variables + b_index, b_value, b_score, b_groups = 999, 999, 999, None + # creates list called features + features = list() + # as long as features list is not as long as square root of total dataset + while len(features) < n_features: + # creates number between 0 and nr of colums (- class) + index = randrange(len(dataset[0])-1) + # add column value if not present yet in features, creates only the index with name of the column + if index not in features: + features.append(index) + # for each column name in list features: + for index in features: + for row in dataset: + # take split point, loops through all the points, selecting 1 feature + groups = test_split(index, row[index], dataset) + # calculates how 'pure' the split is, whether it gives 2 groups that correspond to 2 classes + gini = gini_index(groups, class_values) + # the lower the score is, the better / keep the best score + # you keep reference to current best option + if gini < b_score: + b_index, b_value, b_score, b_groups = index, row[index], gini, groups + # returns a dictionary + return {'index':b_index, 'value':b_value, 'groups':b_groups} + +# Create a terminal node value = node at end of the tree = end leaf +def to_terminal(group): + # returns list of classes of group + outcomes = [row[-1] for row in group] + # selects most popular class; list of outcomes is reduced to 0 or 1; key counts the amount of times 0 or 1 occurs + # selects class based on calculating how many times the class occurs + return max(set(outcomes), key=outcomes.count) + +# Create child splits for a node or make terminals/end leafs +# recursive function, it calls itself +# node is dictionary returned by get_split (b_index, b_value, b_groups) +def split(node, max_depth, min_size, n_features, depth): + left, right = node['groups'] + del(node['groups']) + # check for a no split + # if one of the groups is empty: left and right are both becoming a number with most popular class of the list + # decision is prediction whether sample is class 0 or 1 + if not left or not right: + node['left'] = node['right'] = to_terminal(left + right) + return + # check for max depth / how many levels of nodes you want to have + if depth >= max_depth: + node['left'], node['right'] = to_terminal(left), to_terminal(right) + return + # process left child + # if length of left group is smaller or equal to 1 + if len(left) <= min_size: + # it creates an end leaf + node['left'] = to_terminal(left) + else: + # it tries again to find best split for the reduced group that is at left of the tree at that moment + node['left'] = get_split(left, n_features) + split(node['left'], max_depth, min_size, n_features, depth+1) + # process right child + if len(right) <= min_size: + node['right'] = to_terminal(right) + else: + node['right'] = get_split(right, n_features) + # it tries again to find best split for the reduced group that is at right of the tree at that moment + split(node['right'], max_depth, min_size, n_features, depth+1) + # return no value because functions are working on the same dictionaries + +# Build a decision tree +def build_tree(train, max_depth, min_size, n_features): + # root of decision tree is defined by dictionary of index, value, 2 groups (left/right of the split) + root = get_split(train, n_features) + split(root, max_depth, min_size, n_features, 1) + #root is a Node + # Node: {index: int, value: float, left: Node|TerminalNode, right: Node|TerminalNode} + # TerminalNode: {index: int, value: float, left: int(class), right: int (class)} + return root + +# Make a prediction with a decision tree +# recursive function as well +def predict(node, row): + # node index = column feature, it looks up value for this feature for this row in dataset + # compare feature value of row you're checking with feature value of node + if row[node['index']] < node['value']: + # is it node? + if isinstance(node['left'], dict): + # recursive function at the left + return predict(node['left'], row) + else: + # creates final leaf at the left + return node['left'] + else: + # is it node? + if isinstance(node['right'], dict): + # recursive function at the right + return predict(node['right'], row) + else: + # creates final leaf at the left + return node['right'] + +# Create a random subsample from the dataset with replacement, ratio is called sample_size further on +# This is called BOOTSTRAPPING: build new datasets from the original data, with the same number of rows +# with replacement: after selecting the row we put it back into the data, so it can be selected twice or more +def subsample(dataset, ratio): + sample = list() + # if it is smaller than 1, not all dataset is taken as sample - he uses the full dataset + n_sample = round(len(dataset) * ratio) + while len(sample) < n_sample: + index = randrange(len(dataset)) + sample.append(dataset[index]) + return sample + +# Make a prediction with a list of bagged trees +def bagging_predict(trees, row): + # asks the forest to predict class for every row in the test data, this gives list of votes + predictions = [predict(tree, row) for tree in trees] + # it calculates amount of votes for each class, returns most popular class as prediction + return max(set(predictions), key=predictions.count) + +# Random Forest Algorithm +def random_forest(train, test, max_depth, min_size, sample_size, n_trees, n_features): + trees = list() + for i in range(n_trees): + sample = subsample(train, sample_size) + # building a tree / root is dictionary with index, value, left/right) + tree = build_tree(sample, max_depth, min_size, n_features) + trees.append(tree) + + with open('random_forest_model.json', 'w') as outfile: + json.dump(trees, outfile, indent = 6) + # prediction using one of the folds we separated in the beginning, forest votes on every row of test data + predictions = [bagging_predict(trees, row) for row in test] + # returns votes/predictions of the forest + return(predictions) + +# -------------------------------------- + +# Test the random forest algorithm +seed(2) +# load and prepare data +# filename = 'sonar_csv.csv' +filename = 'iris_data.csv' +dataset = load_csv(filename) +#print(dataset) +# convert string attributes to integers +for i in range(0, len(dataset[0])-1): + str_column_to_float(dataset, i) +# convert class column to integers +#lookup = str_column_to_int(dataset, len(dataset[0])-1) +#print(lookup) + + +# evaluate algorithm +# specifies amounts of subsets for training (-1 for testing) +n_folds = 5 +max_depth = 10 +min_size = 1 +sample_size = 1.0 +# it specifies the size of the subset of features for the folds, where the size is close to the square root of the total number of features +n_features = int(sqrt(len(dataset[0])-1)) +# it tries forest of 1 tree, 5 trees, 10 trees +for n_trees in [1, 5, 10]: + scores = evaluate_algorithm(dataset, random_forest, n_folds, max_depth, min_size, sample_size, n_trees, n_features) + print('Trees: %d' % n_trees) + print('Scores: %s' % scores) + print('Mean Accuracy: %.3f%%' % (sum(scores)/float(len(scores)))) + +#### Note: you also need F1-score & confusion matrix! +#### https://towardsdatascience.com/metrics-to-evaluate-your-machine-learning-algorithm-f10ba6e38234 + +#### how to use this model now? +#### pickle trees +#### use: unpickle trees + bagging_predict with new data + +with open('random_forest_model.json', 'r') as infile: + trees = json.load(infile) + prediction = bagging_predict(trees, dataset[23]) + # this gives a number, you have to reorganise model to get back the string of the class + print(prediction) \ No newline at end of file