def params_4(gamma_settings_4):
# Probability columns
params = Params(gamma_settings_4, spark="supress_warnings")
params._generate_param_dict()
yield params
def sqlite_con_1(gamma_settings_1, params_1):
# Create the database and the database table
con = sqlite3.connect(":memory:")
con.row_factory = sqlite3.Row
cur = con.cursor()
cur.execute("create table test1 (unique_id, mob, surname)")
cur.execute("insert into test1 values (?, ?, ?)", (1, 10, "Linacre"))
cur.execute("insert into test1 values (?, ?, ?)", (2, 10, "Linacre"))
cur.execute("insert into test1 values (?, ?, ?)", (3, 10, "Linacer"))
cur.execute("insert into test1 values (?, ?, ?)", (4, 7, "Smith"))
cur.execute("insert into test1 values (?, ?, ?)", (5, 8, "Smith"))
cur.execute("insert into test1 values (?, ?, ?)", (6, 8, "Smith"))
cur.execute("insert into test1 values (?, ?, ?)", (7, 8, "Jones"))
# Create comparison table
rules = [
"l.mob = r.mob",
"l.surname = r.surname",
]
sql = "select * from test1 limit 1"
cur.execute(sql)
one = cur.fetchone()
columns = one.keys()
sql = _sql_gen_block_using_rules("dedupe_only",
columns,
rules,
table_name_dedupe="test1")
df = pd.read_sql(sql, con)
df = df.drop_duplicates(["unique_id_l", "unique_id_r"])
df = df.sort_values(["unique_id_l", "unique_id_r"])
df.to_sql("df_comparison1", con, index=False)
sql = _sql_gen_add_gammas(gamma_settings_1, table_name="df_comparison1")
df = pd.read_sql(sql, con)
df.to_sql("df_gammas1", con, index=False)
sql = _sql_gen_gamma_prob_columns(params_1, gamma_settings_1, "df_gammas1")
df = pd.read_sql(sql, con)
df.to_sql("df_with_gamma_probs1", con, index=False)
sql = _sql_gen_expected_match_prob(params_1, gamma_settings_1,
"df_with_gamma_probs1")
df = pd.read_sql(sql, con)
df.to_sql("df_with_match_probability1", con, index=False)
sql = _sql_gen_intermediate_pi_aggregate(
params_1, table_name="df_with_match_probability1")
df = pd.read_sql(sql, con)
df.to_sql("df_intermediate1", con, index=False)
sql = _sql_gen_pi_df(params_1, "df_intermediate1")
df = pd.read_sql(sql, con)
df.to_sql("df_pi1", con, index=False)
# Create a new parameters object and run everything again for a second iteration
# Probability columns
gamma_settings_it_2 = copy.deepcopy(gamma_settings_1)
gamma_settings_it_2["proportion_of_matches"] = 0.540922141
gamma_settings_it_2["comparison_columns"][0]["m_probabilities"] = [
0.087438272, 0.912561728
]
gamma_settings_it_2["comparison_columns"][0]["u_probabilities"] = [
0.441543191, 0.558456809
]
gamma_settings_it_2["comparison_columns"][1]["m_probabilities"] = [
0.173315146,
0.326240275,
0.500444578,
]
gamma_settings_it_2["comparison_columns"][1]["u_probabilities"] = [
0.340356209,
0.160167628,
0.499476163,
]
params2 = Params(gamma_settings_it_2, spark="supress_warnings")
params2._generate_param_dict()
sql = _sql_gen_gamma_prob_columns(params2, gamma_settings_it_2,
"df_gammas1")
df = pd.read_sql(sql, con)
df.to_sql("df_with_gamma_probs1_it2", con, index=False)
sql = _sql_gen_expected_match_prob(params2, gamma_settings_it_2,
"df_with_gamma_probs1_it2")
df = pd.read_sql(sql, con)
df.to_sql("df_with_match_probability1_it2", con, index=False)
sql = _sql_gen_intermediate_pi_aggregate(
params2, table_name="df_with_match_probability1_it2")
df = pd.read_sql(sql, con)
df.to_sql("df_intermediate1_it2", con, index=False)
sql = _sql_gen_pi_df(params2, "df_intermediate1_it2")
df = pd.read_sql(sql, con)
df.to_sql("df_pi1_it2", con, index=False)
yield con