def test9():
# making sure dataframe and SQL data base produce the same tree
def Gini_index(p):
return p*(1-p)
test_data2 = np.array([[5, 2, 34, 0], [38, 20, 21, 0], [72, 20, 10, 0], [52, 20, 15, 1]])
node_test = classification_tree.Node(test_data2, Gini_index, [])
node_test2 = sql_tree.SQLTree(cur, 'test_data2', ['column_1', 'column_2', 'column_3'], 'response', Gini_index, [])
assert node_test.past_split == node_test2.past_split
def test6_R2():
# test_data4 = np.array([[5, 34, 1], [38, 7, 1], [72, 15, 0], [52, 29, 1]])
def Gini_index(p):
return p * (1 - p)
node_test = sql_tree.SQLTree(cur, 'test_data4', ['column_1', 'column_2'],
'response', Gini_index, [])
assert node_test.split_variable is None
assert node_test.split_point is None
def test3():
# missing values
# If there is missing values inside the dataset, throw an error.
def Gini_index(p):
return p * (1 - p)
with pytest.raises(ValueError):
sql_tree.SQLTree(cur, 'test_data1', ['column_1', 'column_2'],
'response', Gini_index, [])
def test():
# if responses are all 0 or all 1.
# Making sure the tree ends there.
def Gini_index(p):
return p * (1 - p)
node_test = sql_tree.SQLTree(
cur, 'cancer_zero_test',
['column_1', 'column_2', 'column_3', 'column_4'], 'response',
Gini_index, [])
assert node_test.right is None
assert node_test.left is None
def test7_R3():
# test_data5 = np.array([[16, 24, 34, 1], [38, 20, 21, 0], [14, 20, 10, 1], [52, 20, 15, 1], [32, 59, 1, 0], [34, 21, 69, 1]])
def Gini_index(p):
return p * (1 - p)
node_test = sql_tree.SQLTree(cur, 'test_data5',
['column_1', 'column_2', 'column_3'],
'response', Gini_index, [])
assert node_test.split_variable == 'column_1'
assert node_test.split_point == 16
assert node_test.left.split_variable is None
assert node_test.left.split_point is None
def test8():
# test the smallest value in each column is not the split point
def Gini_index(p):
return p * (1 - p)
predictors = ['column_1', 'column_2', 'column_3', 'column_4']
node_test = sql_tree.SQLTree(cur, 'test_table', predictors, 'response',
Gini_index, [])
for predictor in predictors:
cur.execute('''SELECT min(''' + predictor + ''')'''
'''FROM test_table;''')
test = cur.fetchone()[0]
assert test not in node_test.past_split
def test5_R1():
# making sure the split point is in the first column and be the right point.
# test_data3 = np.array([[5, 0], [38, 0], [72, 0], [52, 1]])
def Gini_index(p):
return p * (1 - p)
node_test = sql_tree.SQLTree(cur, 'test_data3', ['column_1'], 'response',
Gini_index, [])
assert node_test.split_variable == 'column_1'
assert node_test.split_point == 72
assert node_test.left.split_variable == 'column_1'
assert node_test.left.split_point == 52
assert node_test.right.split_variable is None
assert node_test.right.split_point is None
def test2():
# Split feature is the same value.
# making sure don't choose the split variable, which only has one value for all rows
def Gini_index(p):
return p * (1 - p)
node_test = sql_tree.SQLTree(cur, 'test_data',
['column_1', 'column_2', 'column_3'],
'response', Gini_index, [])
# According to the acknowledged node_test's split variables, it has two left split variables, none right split variable
assert node_test.split_variable != 'b'
assert node_test.left.split_variable != 'b'
assert node_test.left.left.split_variable is None
assert node_test.right.split_variable is None
def sql_r3_tree():
cur = st.sqlconnect(host=cd.host,
database=cd.database,
user=cd.user,
password=cd.password)
cur.execute(
"CREATE TABLE IF NOT EXISTS datar3 (v1 FLOAT, v2 FLOAT, v3 FLOAT, y INT);"
)
cur.execute("DELETE FROM datar3;")
cur.execute(
"INSERT INTO datar3 (v1, v2, v3, y) VALUES (0.3, 2.5, 1.79, 1), (2.9, 2.8, 7.1, 1),(2.1, 7.1, 3, 0), (1.1, 18.1, 0.2, 0), (5.1, 6.1, -1, 1), (3.1, -2.8, -3.1, 1), (5.1, 1.1, 1.09, 0), (4.8, -4.1, 0.5, 0)"
)
sqlt = st.SQLTree("datar3", ["v1", "v2", "v3"], "y", cur)
return cur, sqlt
def sql_r2_tree_basic():
cur = st.sqlconnect(host=cd.host,
database=cd.database,
user=cd.user,
password=cd.password)
cur.execute(
"CREATE TABLE IF NOT EXISTS data1 (v1 FLOAT, v2 FLOAT, y INT);")
cur.execute("DELETE FROM data1;")
cur.execute(
"INSERT INTO data1 (v1, v2, y) VALUES (1.5,5,0), (4,2,0), (1,1,0), (2,4,0), (3.5,1,0), (-1.2,6,1), (-2,9,1), (-5,1,1), (-7,1,1), (-6,4,1), (-4,-5,0), (-1,-2,0), (-0.2,-5,0), (5,-1,1);"
)
sqlt = st.SQLTree("data1", ["v1", "v2"], "y", cur)
return cur, sqlt
def test_sql_python_same():
cur = st.sqlconnect(host=cd.host,
database=cd.database,
user=cd.user,
password=cd.password)
x_df = pd.DataFrame([[1.35090528, -0.22763714, 0.62503887],
[-0.0715539, -0.64119863, -0.19062135],
[-1.11177092, 0.50165846, -0.86722735],
[1.24392279, -0.08266315, -0.82700858],
[0.41391078, -1.06708343, -0.591038],
[-0.11328491, 2.19414569, -1.0890808],
[1.00572935, -0.92290436, 1.38861161],
[-0.78596497, 1.56025647, 0.95610325],
[1.59251311, 2.18732072, -0.73577758],
[-1.16918551, -0.21258418, 1.27649019],
[0.70237481, 1.82188747, -0.04181062],
[-0.56060812, 0.56029165, -0.90909157],
[0.44574311, 0.94814604, -0.01507905],
[-1.3072048, 1.62805262, -0.56249722],
[0.62097551, -1.33599419, 0.1845642]],
columns=['v1', 'v2', 'v3'])
y_df = pd.DataFrame([[1], [0], [0], [1], [0], [0], [1], [0], [1], [0], [0],
[1], [0], [1], [0]],
columns=['y'])
tree_ct = ct.DecisionTree(x_df, y_df)
x_test = pd.DataFrame([[0.31269028, 1.86935075, 1.3147904],
[1.47276502, -1.77782668, -0.36375857],
[1.59640162, -1.21098536, -0.07769382],
[-0.40091173, -0.7496455, 0.39000357],
[-0.29370055, -0.40686242, 1.44866448],
[0.06426318, -1.30074211, 0.49274947],
[0.16542666, 0.61140155, -1.94330865]],
columns=['v1', 'v2', 'v3'])
tree_ct_preds = tree_ct.predict(x_test)
tree_preds_df = pd.concat([
x_test,
pd.DataFrame([int(p) for p in tree_ct_preds], columns=['preds'])
],
axis=1).sort_values('v1')
tree_preds_df.index = range(tree_preds_df.shape[0])
cur.execute(
"CREATE TABLE IF NOT EXISTS datatable (v1 FLOAT, v2 FLOAT, v3 FLOAT, y INT);"
)
cur.execute("DELETE FROM datatable;")
a = "INSERT INTO datatable (v1, v2, v3, y) VALUES (1.35090528, -0.22763714, 0.62503887, 1), "
b = "(-0.0715539 , -0.64119863, -0.19062135, 0), (-1.11177092, 0.50165846, -0.86722735, 0),"
c = "(1.24392279, -0.08266315, -0.82700858, 1), (0.41391078, -1.06708343, -0.591038, 0),"
d = "(-0.11328491, 2.19414569, -1.0890808, 0),(1.00572935, -0.92290436, 1.38861161, 1),"
e = "(-0.78596497, 1.56025647, 0.95610325, 0),(1.59251311, 2.18732072, -0.73577758, 1),"
f = "(-1.16918551, -0.21258418, 1.27649019, 0),(0.70237481, 1.82188747, -0.04181062, 0),"
g = "(-0.56060812, 0.56029165, -0.90909157, 1), (0.44574311, 0.94814604, -0.01507905, 0),"
h = "(-1.3072048 , 1.62805262, -0.56249722, 1),(0.62097551, -1.33599419, 0.1845642, 0);"
query_data = a + b + c + d + e + f + g + h
cur.execute(query_data)
tree_st = st.SQLTree("datatable", ['v1', 'v2', 'v3'], 'y', cur)
cur.execute(
"CREATE TABLE IF NOT EXISTS testtable (v1 FLOAT, v2 FLOAT, v3 FLOAT);")
cur.execute("DELETE FROM testtable;")
i = "INSERT INTO testtable (v1, v2, v3) VALUES (0.31269028, 1.86935075, 1.3147904), "
j = "(1.47276502, -1.77782668, -0.36375857), (1.59640162, -1.21098536, -0.07769382), "
k = "(-0.40091173, -0.7496455, 0.39000357), (-0.29370055, -0.40686242, 1.44866448), "
l = "(0.06426318, -1.30074211, 0.49274947), (0.16542666, 0.61140155, -1.94330865);"
query_test = i + j + k + l
cur.execute(query_test)
tree_st.predict("testtable")
cur.execute("SELECT * FROM testtable;")
preds_sql = cur.fetchall()
preds_df_sql = pd.DataFrame(preds_sql, columns=['v1', 'v2', 'v3',
'preds']).sort_values('v1')
preds_df_sql.index = range(preds_df_sql.shape[0])
assert tree_preds_df.equals(preds_df_sql)
# We should also get the same results if we prune at the same level
tree_ct.prune(alphas=[0.2], cross_validate=False)
tree_st.prune(alpha=0.2)
# Python preds, pruned
tree_ct_preds_pruned = tree_ct.predict(x_test)
tree_preds_df_pruned = pd.concat([
x_test,
pd.DataFrame([int(p) for p in tree_ct_preds_pruned], columns=['preds'])
],
axis=1).sort_values('v1')
tree_preds_df_pruned.index = range(tree_preds_df_pruned.shape[0])
# SQL preds, pruned
cur.execute("ALTER TABLE testtable DROP COLUMN preds;")
tree_st.predict("testtable")
cur.execute("SELECT * FROM testtable;")
preds_sql_pruned = cur.fetchall()
preds_df_sql_pruned = pd.DataFrame(preds_sql_pruned,
columns=['v1', 'v2', 'v3',
'preds']).sort_values('v1')
preds_df_sql_pruned.index = range(preds_df_sql_pruned.shape[0])
assert tree_preds_df_pruned.equals(preds_df_sql_pruned)