def test_updated_from_namespaces():
composer_child = Composer().update(a=lambda: 5,
c=lambda b: b * 2).link(b="a")
composer = (Composer().update_namespaces(
x=composer_child,
y=composer_child).link(outer_x="x__c", outer_y="y__c").update(
final=lambda outer_x, outer_y: outer_x + outer_y))
assert composer.final() == 20
def generate_random_graph(graph_size=42):
functions = []
def function_0():
return 42
functions.append(function_0)
for i in range(1, graph_size):
fn_name = f"function_{i}"
num_args = randint(0, min(5, i - 1))
arg_names = set()
while len(arg_names) < num_args:
arg_name = f"function_{randint(0, i-1)}"
if arg_name not in arg_names:
arg_names.add(arg_name)
if arg_names:
body = " + ".join(arg_names)
else:
body = str(randint(0, 100))
exec(
dedent(f"""
def {fn_name}({', '.join(sorted(arg_names))}):
return {body}
"""))
functions.append(locals()[fn_name])
return Composer().update(*functions)
def test_empty_kwargs():
a = 1
b = 2
result = a + b + sum(a * 5 for i in range(5))
def d(a, *d_, b, **c_):
return a + sum(d_) + b + sum(c_.values())
composer = (Composer().update_parameters(
a=1, b=2).update(**{f"d_{i}": lambda a: a * 5
for i in range(5)}).update(d))
assert composer.d() == result
def get_cheaper_cars(car_prices, your_car_price):
df = car_prices
return df[df.price < your_car_price]
def get_savings_on_cheaper_cars(cheaper_cars, mean_car_price):
return cheaper_cars.assign(savings=lambda df: mean_car_price - df.price)
def get_burger_savings(savings_on_cheaper_cars, price_of_a_burger):
return savings_on_cheaper_cars.assign(
burgers_saved=lambda df: df.savings / price_of_a_burger)
def get_savings_histogram(burger_savings):
return px.histogram(burger_savings, x="burgers_saved")
f = (Composer().update_without_prefix(
"get_",
get_car_prices,
get_cheaper_cars,
get_mean_car_price,
get_savings_on_cheaper_cars,
get_burger_savings,
get_savings_histogram,
).update_parameters(your_car_price=(int, 100_000),
price_of_a_burger=(float, 100)))
def test_updated_from_links():
composer_a = Composer().update(a=lambda: 5, c=lambda b: b * 2).link(b="a")
composer_b = Composer().update_from(composer_a).update(d=lambda b: b * 3)
assert composer_b.d() == 15
def test_basic_links():
composer = Composer().update(a=lambda: 5, c=lambda b: b * 2).link(b="a")
assert composer.c() == 10
def test_call_link():
composer = Composer().update(a=lambda: 5, c=lambda b: b * 2).link(b="a")
assert composer.b() == 5
def function_37():
return 19
def function_38(function_33, function_34):
return function_34 + function_33
def function_39(function_1, function_10, function_25):
return function_25 + function_1 + function_10
def function_40(function_21, function_24, function_33):
return function_21 + function_33 + function_24
def function_41(function_18):
return function_18
#%%
scope = locals()
functions = [
scope[f"function_{i}"] for i in range(42) if f"function_{i}" in scope
]
large_graph = (Composer().update(*functions).link(function_8="function_7",
function_9="function_5"))
"An example with a broken composer."
#%%
from pathlib import Path
from random import choice, random
import pandas as pd
import numpy as np
import plotly.express as px
import math
from fn_graph import Composer
prices = [random() * 100_000 + 50000 for _ in range(10)]
f = (Composer().update(
wave=lambda frequency, amplitude: pd.DataFrame(dict(x=range(501))).assign(
y=lambda df: amplitude * np.cos(df.x / 500 * math.pi * 3) * np.sin(
df.x / 500 * math.pi * frequency)),
plot=lambda wave: px.line(wave, x="x", y="y"),
broken=lambda wave, missing: wave,
).update_parameters(frequency=(float, 1), amplitude=(float, 1)))
# %%
return 5
def b(data, factor):
return data * factor
def c(b):
return b
def combined_result(child_one__c, child_two__c):
pass
child = Composer().update(b, c)
parent = (Composer().update_namespaces(
child_one=child, child_two=child).update(
data, combined_result).update_parameters(child_one__factor=3,
child_two__factor=5))
# %%
parent.graphviz()
# %%
# Link example
def calculated_factor(data):
return data / 2
# Introductory example
def a():
return 5
def b(a):
return a * 5
def c(a, b):
return a * b
composer = Composer().update(a, b, c)
# Call any result
composer.c() # 125
composer.a() # 5
composer.graphviz().render("intro.gv", format="png")
# Some pure functions
def get_car_prices():
df = pd.DataFrame(
dict(
model=[
choice(["corolla", "beetle", "ferrari"]) for _ in range(10)
],
def plot_total_market_cap(total_market_cap):
"""
Plot the total market cap
"""
return px.line(total_market_cap, x="datetime", y="market_cap")
def plot_market_cap_changes(total_market_cap_change):
"""
Plot the market cap changes
"""
return px.bar(
total_market_cap_change,
x="datetime",
y="market_cap_change",
color="change_classification",
)
f = (Composer().update_parameters(swing_threshold=0.1 * 10**12).update(
share_prices,
daily_share_prices,
shares_in_issue,
market_cap,
total_market_cap,
total_market_cap_change,
plot_market_caps,
plot_total_market_cap,
plot_market_cap_changes,
))
def confusion_matrix(predictions, test_target):
"""
Show the confusion matrix
"""
cm = sklearn.metrics.confusion_matrix(test_target, predictions)
return plot_confusion_matrix(cm, ["setosa", "versicolor", "virginica"])
f = (
Composer().update_parameters(
# Parameter controlling the model type (ols, svc)
model_type="olm",
# Parameter enabling data preprocessing
do_preprocess=True,
).update(
iris,
data,
preprocess_data,
investigate_data,
split_data,
training_features,
training_target,
test_features,
test_target,
model,
predictions,
classification_metrics,
confusion_matrix,
))
def test_default_arguments():
composer = Composer().update(c=lambda a, b=3: a + b).update_parameters(a=1)
assert composer.c() == 4
composer = Composer().update(c=lambda a, b=3: a + b).update_parameters(a=1,
b=2)
assert composer.c() == 3
def test_simple_parameters():
composer = (Composer().update(c=lambda a, b: a + b).update_parameters(
a=1, b=(int, 2)))
assert composer.c() == 3
