### Setup Virtual Environment and Install Dependencies

Source: https://github.com/dedupeio/dedupe-examples/blob/main/README.md

Commands to set up a virtual environment named 'dedupe-examples' and install project dependencies from 'requirements.txt'. This ensures a consistent development environment.

```bash
mkvirtualenv dedupe-examples
pip install -r requirements.txt
workon dedupe-examples
```

--------------------------------

### Run MySQL Example Script

Source: https://github.com/dedupeio/dedupe-examples/blob/main/README.md

Instructions to run the MySQL example, which demonstrates deduplication on IL campaign contributions data stored in MySQL. Refer to 'mysql_example/README.md' for detailed setup and execution.

```bash
cd mysql_example
# Refer to README.md for specific commands
```

--------------------------------

### Install Dependencies for Gazetteer Examples

Source: https://github.com/dedupeio/dedupe-examples/blob/main/gazetteer_example/README.md

Installs the necessary Python dependencies for the in-memory Gazetteer example using pip and a requirements file. It assumes a virtual environment is set up for dependency management.

```bash
pip install -r requirements-2.x.txt
```

```bash
pip install -r requirements-1.x.txt
```

--------------------------------

### Setup PostgreSQL Database for Gazetteer Example

Source: https://github.com/dedupeio/dedupe-examples/blob/main/gazetteer_example/README.md

Sets up a PostgreSQL database named 'dedupe_example' and exports the connection string as an environment variable. This is a prerequisite for running the PostgreSQL-backed Gazetteer example.

```bash
createdb dedupe_example
export DATABASE_URL=postgres:///dedupe_example
```

--------------------------------

### Run CSV Example Script

Source: https://github.com/dedupeio/dedupe-examples/blob/main/README.md

Steps to navigate into the CSV example directory, install the 'unidecode' library, and run the 'csv_example.py' script. This script demonstrates deduplication on a list of early childhood education sites.

```bash
cd csv_example
pip install unidecode
python csv_example.py
```

--------------------------------

### Run Dedupe.io Example Scripts

Source: https://github.com/dedupeio/dedupe-examples/blob/main/mysql_example/README.md

These bash commands execute the Python scripts to initialize the MySQL database and run the deduplication example. Ensure dependencies are installed and MySQL configuration is complete before running.

```bash
cd mysql_example
python mysql_init_db.py 
python mysql_example.py
```

--------------------------------

### Run Patent Example Script

Source: https://github.com/dedupeio/dedupe-examples/blob/main/README.md

Instructions to run the patent example script. This involves navigating to the 'patent_example' directory, installing 'unidecode', and executing the Python script to process patent data.

```bash
cd patent_example
pip install unidecode
python patent_example.py
```

--------------------------------

### Run Gazetteer Example Script

Source: https://github.com/dedupeio/dedupe-examples/blob/main/README.md

Commands to run the gazetteer example script. This example uses the Gazetteer class to link entries between two spreadsheets of electronics products.

```bash
cd gazetteer_example.py
python gazetteer_example.py
```

--------------------------------

### Clone Dedupe Examples Repository

Source: https://github.com/dedupeio/dedupe-examples/blob/main/README.md

Instructions to clone the dedupe-examples repository using Git. This is the first step to obtain the example scripts.

```bash
git clone https://github.com/dedupeio/dedupe-examples.git
cd dedupe-examples
```

--------------------------------

### Run PostgreSQL Big Dedupe Example Script

Source: https://github.com/dedupeio/dedupe-examples/blob/main/README.md

Steps to run the PostgreSQL example, which is a port of the MySQL campaign contributions example to handle a large dataset on PostgreSQL. Consult 'pgsql_big_dedupe_example/README.md' for details.

```bash
cd pgsql_big_dedupe_example
# Refer to README.md for specific commands
```

--------------------------------

### Initialize MySQL Database with Dedupe.io

Source: https://github.com/dedupeio/dedupe-examples/blob/main/mysql_example/README.md

This script initializes the MySQL database for the Dedupe.io campaign contribution example. It requires a pre-created 'contributions' database and correct MySQL connection details in 'mysql.cnf'. The script populates the database with sample data for deduplication.

```python
from __future__ import print_function

import os
import sys
import time
import re

from unidecode import unidecode

from fuzzy import DamerauLevenshtein

from mysql import connector
from mysql.connector import errorcode

import dedupe

def unicode_csv_dict_reader(unicode_csv_data, **kwargs):
    # csv.reader expects strings
    for row in unicode_csv_data:
        yield [unicode(s, "utf-8") for s in row]


def preloaded_mysql_example(db_config):

    # load up our training data
    with open('mysql_example/names.csv') as f:
        reader = csv.reader(f)
        header = [h.strip() for h in next(reader)]

        data_d = {}
        for row in reader:
            row = dict(zip(header, row))
            data_d[row['id']] = row

    # ## Create a distinct and human readable dataset
    print('Creating a distinct and human readable dataset')

    # Create the canonical record
    def canonical(fields):
        return str(unidecode(fields['firstname'].strip().lower()) +
                   fields['lastname'].strip().lower() +
                   fields['zip'].strip())

    # Define the comparison fields
    dedupe.convenience.write_settings(data_d, canonical, header, 100000, 'mysql_example/mysql_learned_settings')

    # ## If we have training data, train the model. Otherwise, use the learned settings.
    if os.path.exists('mysql_example/mysql_model'):
        print('using trained model')

        with open('mysql_example/mysql_model', 'rb') as f:
            lda = dedupe.StaticDedupe(f)
    else:
        print('creating new model')
        lda = dedupe.Dedupe(fields, num_runs=20)
        lda.prepare_training(data_d, 'id')

        print('found training data')
        lda.train()

        # save our trained model
        with open('mysql_example/mysql_model', 'wb') as f:
            lda.write_model(f)

        # save our weights
        lda.write_settings('mysql_example/mysql_learned_settings')

    # ## Linking
    print('linking records')
    # MySQL connection details
    try:
        conn = connector.connect(**db_config)
        cur = conn.cursor(dictionary=True)
        cur.execute("SELECT * FROM contributions.contributions")
        results = cur.fetchall()
        cur.close()
        conn.close()
    except connector.Error as err:
        if err.errno == errorcode.ER_ACCESS_DENIED_ERROR:
            print("Something is wrong with your user name or password")
        elif err.errno == errorcode.ER_BAD_DB_ERROR:
            print("Database does not exist")
        else:
            print(err)
        sys.exit(1)

    # create a gazetteer
    # Use mysql to get the data
    # Note: for large data, consider using a database cursor to stream results
    # and match in chunks
    
    print('importing data from mysql')
    
    # create a dictionary of records from the mysql database
    # where the key is the mysql primary key
    data_from_mysql = {}
    try:
        conn = connector.connect(**db_config)
        cur = conn.cursor(dictionary=True)
        cur.execute("SELECT * FROM contributions.contributions")
        results = cur.fetchall()
        cur.close()
        conn.close()
    except connector.Error as err:
        if err.errno == errorcode.ER_ACCESS_DENIED_ERROR:
            print("Something is wrong with your user name or password")
        elif err.errno == errorcode.ER_BAD_DB_ERROR:
            print("Database does not exist")
        else:
            print(err)
        sys.exit(1)

    for row in results:
        data_from_mysql[row['id']] = row

    print('loaded %s records from mysql' % len(data_from_mysql))

    # create a gazetteer object
    # The gazetteer takes a dictionary of records, and a function that
    # returns a canonical form for each record
    gazetteer = dedupe.Gazetteer(data_from_mysql, canonical)

    # The gazetteer can be linked against the trained model
    # it returns a generator of matches
    print('matching...')
    start_time = time.time()

    # The linking functions returns a generator
    # The results are pairs of record IDs that are duplicates
    # For example: ((record_id_1, record_id_2), confidence_score)
    linked_records = lda.match(gazetteer)

    # ## Write the results to a file
    print('writing results to output/mysql_output.csv')
    # Open up a file to write the duplicate data to
    with open('output/mysql_output.csv', 'w', newline='') as outfile:
        # create a csv writer object
        # header for output file
        csv_writer = csv.writer(outfile)
        csv_writer.writerow(['id_1', 'id_2', 'confidence'])

        # write all the duplicate pairs to the csv file
        for (record_id_1, record_id_2), confidence in linked_records:
            csv_writer.writerow([record_id_1, record_id_2, confidence])

    print('done')
    print('elapsed time: %s' % (time.time() - start_time))


if __name__ == '__main__':
    # ## Prepare the database
    # We need to create a database that we can run the example from
    # The database should be called 'contributions'

    # ## Create MySQL connection
    db_config = {
        'user': 'your_username',
        'password': 'your_password',
        'host': '127.0.0.1',
        'port': 3306,
        'database': 'contributions'
    }

    # ## If you have a mysql.cnf file with connection details, you can use that instead
    # db_config = dedupe.convenience.mysql_config_to_dict('mysql_example/mysql.cnf')

    preloaded_mysql_example(db_config)

```

--------------------------------

### Run Gazetteer Example Evaluation

Source: https://github.com/dedupeio/dedupe-examples/blob/main/gazetteer_example/README.md

Executes the Python script for evaluating the in-memory Gazetteer matching example. This script is used to assess the performance and accuracy of the matching job.

```bash
python gazetteer_evaluation.py
```

--------------------------------

### Run PostgreSQL Gazetteer Example

Source: https://github.com/dedupeio/dedupe-examples/blob/main/gazetteer_example/README.md

Executes the Python script for the PostgreSQL-backed Gazetteer matching example. This script interacts with a PostgreSQL database to perform matching and updates relevant tables.

```bash
python gazetteer_postgres_example.py
```

--------------------------------

### Run Record Linkage Example Script

Source: https://github.com/dedupeio/dedupe-examples/blob/main/README.md

Steps to execute the record linkage example. This involves changing to the 'record_linkage_example' directory and running the Python script to link matching entries between two spreadsheets of electronics products.

```bash
cd record_linkage_example
python record_linkage_example.py
```

--------------------------------

### Run In-Memory Gazetteer Example

Source: https://github.com/dedupeio/dedupe-examples/blob/main/gazetteer_example/README.md

Executes the Python script for the in-memory Gazetteer matching example. This script performs matching entirely within the application's memory and produces 'gazetteer_output.csv'.

```bash
python gazetteer_example.py
```

--------------------------------

### Example Labeling Operation for Dedupe Training

Source: https://github.com/dedupeio/dedupe-examples/blob/main/README.md

Illustrates a typical interactive labeling session for training the dedupe model. Users are presented with pairs of records and asked to classify them as matches, non-matches, or unsure.

```text
Phone :  2850617
Address :  3801 s. wabash
Zip : 
Site name :  ada s. mckinley st. thomas cdc

Phone :  2850617
Address :  3801 s wabash ave
Zip : 
Site name :  ada s. mckinley community services - mckinley - st. thomas

Do these records refer to the same thing?
(y)es / (n)o / (u)nsure / (f)inished
```

--------------------------------

### Run Patent Disambiguation Example

Source: https://github.com/dedupeio/dedupe-examples/blob/main/patent_example/README.md

Executes the Python script for patent data disambiguation using the 'dedupe' library. No external dependencies beyond Python and the 'dedupe' library are required for this command.

```shell
python patent_example.py
```

--------------------------------

### Evaluate Patent Disambiguation Precision/Recall

Source: https://github.com/dedupeio/dedupe-examples/blob/main/patent_example/README.md

Runs a Python script to evaluate the precision and recall of the patent data disambiguation against provided reference data. Requires the 'dedupe' library and the reference CSV file.

```shell
python patent_evaluation.py
```

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