270 lines
8.6 KiB
Python
270 lines
8.6 KiB
Python
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import dataclasses
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import json
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import numpy as np
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import pandas as pd
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import streamlit as st
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from scipy.stats import distributions as dist
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from scipy.stats import gaussian_kde as gkde
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import plotly.express as px
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import plotly.graph_objects as go
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# st.set_page_config(layout="wide")
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titles = ["Architect", "Engineer", "Sr. Engineer"]
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num_samples = int(1e4)
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@dataclasses.dataclass(frozen=True)
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class Employee:
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name: str = "John Doe"
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title: str = "unknown"
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region: int = 0
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salary: float = 0.0
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score: float = 0.5
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def __eq__(cls, other_cls):
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return cls.name == other_cls.name
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def __lt__(cls, other_cls):
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return cls.name < other_cls.name
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default_employees = [
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Employee("Alice", "Architect"),
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Employee("Bob", "Architect"),
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Employee("Cher", "Engineer"),
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Employee("David", "Sr. Engineer"),
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Employee("Eirene", "Engineer"),
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Employee("Fiona", "Sr. Engineer"),
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Employee("Gavin", "Engineer"),
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]
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# for i in range(5000):
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# default_employees.append(Employee(f"Gavin {i}", np.random.choice(titles)))
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try:
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data = json.load(open("data.json", "r"))
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except FileNotFoundError:
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data = [
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{"title": "Architect", "salary": [50000, 100000], "raise": [10, 20]},
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{"title": "Engineer", "salary": [50000, 100000], "raise": [10, 20]},
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{"title": "Sr. Engineer", "salary": [50000, 100000], "raise": [10, 20]},
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]
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titles = [d["title"] for d in data]
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salaries = {d["title"]: d["salary"] for d in data}
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increases = {d["title"]: d["raise"] for d in data}
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# increases = {title: (10, 20) for title in titles}
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# salaries = {title: (50000, 100000) for title in titles}
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st.title("Payroll Calculator")
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budget = st.sidebar.number_input(
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"Maximum Payroll (dollars per year)", value=550000, step=5000
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)
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if "employees" not in st.session_state:
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st.session_state["employees"] = set(default_employees)
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def add_employee(employee_name, title="unknown", region=0, salary=0, score=0):
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if "employees" in st.session_state and employee_name:
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remove_employee(employee_name)
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st.session_state["employees"].add(
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Employee(employee_name, title, region, salary, score)
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)
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def remove_employee(employee_name):
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if "employees" in st.session_state and employee_name:
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for e in st.session_state["employees"]:
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if e.name == employee_name:
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st.session_state["employees"].remove(e)
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break
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if "increases" not in st.session_state:
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st.session_state["increases"] = increases
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if "salary_ranges" not in st.session_state:
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st.session_state["salary_ranges"] = salaries
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with st.expander("Roles"):
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title = st.selectbox("Position", options=titles)
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_inc = st.select_slider(
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f"Percentage Increase for {title}",
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value=st.session_state.increases[title],
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options=np.arange(0, 100),
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)
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_sal = st.select_slider(
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f"Salary Range for {title}",
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value=st.session_state.salary_ranges[title],
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options=np.arange(50000, 250001, 5000),
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)
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if st.button("Set"):
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st.session_state.increases[title] = [int(i) for i in _inc]
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st.session_state.salary_ranges[title] = [int(i) for i in _sal]
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st.markdown("Updated role definition.")
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with st.sidebar.expander("THE RED BOX"):
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a = st.slider("Upper", value=3.0, min_value=1.0, max_value=3.0, step=0.25)
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b = st.slider("Lower", value=1.0, min_value=1.0, max_value=3.0, step=0.25)
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c = st.slider("%MAX", value=0.90, min_value=0.5, max_value=1.0, step=0.05)
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with st.expander("Employees"):
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st.markdown("You can consider promotions here as well.")
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employee_title = st.selectbox("Employee position", options=titles)
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employee_name = st.text_input("Employee name")
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col1, col2, col3 = st.columns(3)
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with col1:
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salary = st.number_input("salary (optional)", value=0)
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with col3:
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region = st.number_input("region", value=0)
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with col2:
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performance = st.slider("performance", value=0.5)
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add_new_employee = st.button("Add or update employee")
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if add_new_employee:
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add_employee(employee_name, employee_title, region, salary, performance)
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rem_employee = st.button("Remove employee")
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if rem_employee:
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remove_employee(employee_name)
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st.sidebar.markdown("### Employee Roster")
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if st.session_state.get("employees"):
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st.sidebar.write([e.__dict__ for e in sorted(st.session_state["employees"])])
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employees = st.session_state.employees
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# employees = default_employees
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increases = st.session_state.increases
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salary_ranges = st.session_state.salary_ranges
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forecast = st.button("Forecast")
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samples = {}
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import multiprocessing as mp
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import numpy as np
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from copy import deepcopy
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# st.write(employees == set(default_employees))
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ss = np.random.SeedSequence()
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def random_sampling(A):
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rng, employee, num_samples, salary_ranges, increases = A
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# return rng.random(int(num_samples))
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# return employee.name
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e = employee
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sample = {}
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# TODO: revisit by zip code / region. USE salary_ranges[region][title]
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mnS, mxS = salary_ranges[e.title]
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if e.salary == 0: # simulate salary if unspecified
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current_salary = rng.random(int(num_samples)) * (mxS - mnS) + mnS
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else:
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current_salary = np.ones(num_samples) * e.salary
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mnI, mxI = increases[e.title]
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# TODO: revisit how score is used.
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# now: up to 10% linear increase based on performance, must be over 0.5
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random_increase = (100 + rng.random(int(num_samples)) * (mxI - mnI) + mnI) / 100
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if e.score > 0.5:
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random_increase *= (e.score - 0.5) / 5
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sample["inc"], sample["old"], sample["new"] = (
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random_increase,
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current_salary,
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np.minimum(current_salary * random_increase, mxS),
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)
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return (e.name, sample)
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if forecast:
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# n_proc = min(max(( 1, mp.cpu_count() - 1 )), len(employees))
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n_proc = 8
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pool = mp.Pool(processes=n_proc)
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child_seeds = ss.spawn(len(employees))
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st.sidebar.write("Exploring Possibilities")
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# samples_raw = pool.starmap(f, [ (np.random.default_rng(s), e, num_samples, salary_ranges, increases) for s, e in zip(child_seeds, employees) ])
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salary_ranges = st.session_state.salary_ranges
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increases = st.session_state.increases
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st.write(salary_ranges, increases)
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samples_raw = map(
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random_sampling,
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[
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(np.random.default_rng(s), e, num_samples, salary_ranges, increases)
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for s, e in zip(child_seeds, employees)
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],
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)
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samples = {s[0]: s[1] for s in samples_raw}
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st.sidebar.write("Predicting Budgets")
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old_salaries = np.array([samples[s]["old"] for s in samples]).T
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new_salaries = np.array([samples[s]["new"] for s in samples]).T
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old_payroll = old_salaries.sum(axis=1)
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new_payroll = new_salaries.sum(axis=1)
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fig = go.Figure()
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mn, mx = round(old_payroll.min()), round(new_payroll.max())
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fig.add_trace(
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go.Histogram(x=old_payroll, histnorm="probability density", name="before")
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)
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fig.add_trace(
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go.Histogram(
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x=new_payroll,
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histnorm="probability density",
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name="after",
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marker_color="yellow",
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)
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)
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fig.add_vrect(
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x0=c * budget,
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x1=budget,
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line_color="red",
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line_width=5,
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annotation_text="budget",
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annotation_position="left",
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)
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fig.update_layout(
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title="Salary Forecast",
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xaxis_title="Required Amount ($)",
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yaxis_title="",
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font=dict(family="Courier New, monospace", size=18, color="#7f7f7f"),
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)
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st.sidebar.write("Performing Analysis")
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kde = gkde(np.random.choice(new_payroll, num_samples // 5))
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predicted_density = kde.pdf(new_payroll)
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observed_density = dist.beta(a=a, b=b, loc=c * budget, scale=(1 - c) * budget).pdf(
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new_payroll
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)
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ratio = observed_density / predicted_density
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ratio = ratio / max(ratio)
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accepted_inds = [r for r in range(num_samples) if np.random.rand() < ratio[r]]
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new_salaries_updated = new_payroll[accepted_inds]
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fig.add_trace(
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go.Histogram(
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x=new_salaries_updated, histnorm="probability density", name="options"
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)
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)
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fig.update_layout(
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legend=dict(
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orientation="h",
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yanchor="top",
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xanchor="right",
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y=1,
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x=1,
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)
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)
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st.plotly_chart(fig, use_container_width=True)
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st.markdown(f"Summary of {len(accepted_inds)} feasible new salaries (ranked)")
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df = pd.DataFrame(new_salaries[accepted_inds, :], columns=sorted(samples.keys()))
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df["total"] = new_payroll[accepted_inds]
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df = df.astype(int)
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st.write(df.sort_values("total").reset_index(drop=True))
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