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March 14, 2020 23:00
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Three ways to mine frequent patterns...
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| #!pip install mlxtend | |
| import pandas as pd | |
| import csv | |
| from mlxtend.preprocessing import TransactionEncoder | |
| from mlxtend.frequent_patterns import apriori | |
| def csv_to_tabular(csv_path): | |
| """ | |
| Given a path to a CSV data file, return a tabular dataset, and its | |
| column names. | |
| For example, given a CSV file containing: | |
| eggs,cheese,bread | |
| eggs,milk | |
| Return: | |
| column_names=['eggs','cheese','bread','milk'] | |
| tabular_dataset=[[1,1,1,0],[1,0,0,1]] | |
| That is, convert the CSV rows into a num_items x num_rows matrix | |
| of 1=present, 0=absent values. | |
| """ | |
| dataset = list() | |
| column_names = set() | |
| with open(csv_path, "r") as csvfile: | |
| reader = csv.reader(csvfile, delimiter=",", quotechar='"') | |
| for row_number, items in enumerate(reader): | |
| # Drop empty items | |
| items = [i for i in items if i] | |
| if not items: | |
| # No non-empty items in this row, skip it! | |
| continue | |
| transaction = set(items) | |
| dataset.append(transaction) | |
| # Add any unique item names to our list of column names. | |
| column_names |= transaction | |
| # Lock the column name order. | |
| column_names = list(column_names) | |
| tabular_dataset = [ | |
| [1 if col_name in transaction else 0 for col_name in column_names] | |
| for transaction in dataset | |
| ] | |
| return tabular_dataset, column_names | |
| def one(): | |
| tabular_dataset, column_names = csv_to_tabular("supermarket_trans.csv") | |
| te = TransactionEncoder() | |
| te_ary = te.fit(tabular_dataset).transform(tabular_dataset) | |
| df = pd.DataFrame(tabular_dataset, columns=column_names) | |
| frequent_itemsets = apriori(df, min_support=0.1, use_colnames=True) | |
| print(frequent_itemsets) | |
| def two(): | |
| def csv_lines(csv_path_name): | |
| with open(csv_path_name) as csv_file: | |
| reader = csv.reader(csv_file) | |
| for row in reader: | |
| # Drop empty values | |
| yield [value for value in row if value] | |
| dataset = [row for row in csv_lines("supermarket_trans.csv")] | |
| te = TransactionEncoder() | |
| te_ary = te.fit(dataset).transform(dataset) | |
| df = pd.DataFrame(te_ary, columns=te.columns_) | |
| # print(df) | |
| frequent_itemsets = apriori(df, min_support=0.1, use_colnames=True) | |
| print(frequent_itemsets) | |
| def three(): | |
| dataset = list() | |
| with open('supermarket_trans.csv', 'r') as csvfile: | |
| reader = csv.reader(csvfile, delimiter=',',quotechar='"') | |
| for row in reader: | |
| if len(row) > 0: | |
| dataset.append(row) | |
| # print(len(dataset)) | |
| te = TransactionEncoder() | |
| te_ary = te.fit(dataset).transform(dataset) | |
| df = pd.DataFrame(te_ary, columns=te.columns_) | |
| # print(df) | |
| frequent_itemsets = apriori(df, min_support=0.1, use_colnames=True) | |
| print(frequent_itemsets) | |
| def one(): | |
| tabular_dataset, column_names = csv_to_tabular("supermarket_trans.csv") | |
| te = TransactionEncoder() | |
| te_ary = te.fit(tabular_dataset).transform(tabular_dataset) | |
| df = pd.DataFrame(tabular_dataset, columns=column_names) | |
| # print(df) | |
| frequent_itemsets = apriori(df, min_support=0.1, use_colnames=True) | |
| print(frequent_itemsets) | |
| one() | |
| two() | |
| three() |
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