Michael Pilosov
3 years ago
8 changed files with 356 additions and 0 deletions
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data.json |
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README.md |
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FROM python:3.9.3 |
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WORKDIR /app |
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COPY app.py ./app.py |
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COPY data.json ./data.json |
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COPY requirements.txt ./requirements.txt |
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RUN pip3 install -r requirements.txt |
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# swap health-check endpoint to be where GCP looks for it by default |
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# RUN find /usr/local/lib/python3.9/site-packages/streamlit -type f \( -iname \*.py -o -iname \*.js \) -print0 | xargs -0 sed -i 's/healthz/health-check/g' |
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run: |
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./st run app.py |
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install: |
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pip install -r requirements.txt |
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build: |
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docker build -t streamlit:latest . |
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.PHONY: run install build |
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# Salary Assessment App |
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|
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Demonstration of an interactive web-application built in python. |
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This application takes in an employee roster (built interactively, best for small teams), with names, positions, and salaries for each employee. |
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It then allows someone (e.g. a manager) to simulate promotions and/or salary raises and see the overall impact on the final payroll budget (visualized as a pair of overlaying distributions which show the before/after scenario under the provided ranges). |
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Finally, the app "solves the problem" of assigning a new salary to each employee while staying below some pre-defined budget for payroll, using a novel Monte-Carlo method implemented directly within the app itself (rather than relying on importing another library). |
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> This example shows "a small amount of effort" exerted to ensure reproducibility and readability but ultimately lacks in overall user-friendliness. |
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Takeaways: *Good reproducibility, passable style/formatting.* |
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- Of particular note here is the executable `run` shell script which provides support for running the application using a docker image as well as simultaneously supporting native shell execution if `docker` is not in the system `$PATH` |
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- Note the style in which functions are written in `app.py` |
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- There is a mixture of functions and procedural code, wide abuse of global variables, and a lot of messy plotting code. Is it readable overall? |
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- The app's "state" is held in `data.json`, is saved in a human-readable format (as opposed to binary), and is small enough to be negligible |
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- The functions that are defined at least have readable names |
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- If the app grew any larger than this, one might be wise to migrate functions into a separate module and import it in `app.py` |
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- Note the lack of a proper `README` / we just "presume" the user knows what to do with the presence of a `Makefile`. Is this acceptable in your opinion? |
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- Very minimal `Makefile` is "sort of self-documenting" but many people don't know to look there. It could use documentation so that `make help` at least produces some sort of a helpful guide |
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## Usage |
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(if using local `python` instead of `docker`): |
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``` |
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make install |
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``` |
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then |
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```bash |
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make run |
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``` |
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and visit `localhost:8501` (or `<tld>/<user>/proxy/8501/` in Jupyter if you have `jupyter-server-proxy` installed to access the app via proxy) |
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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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[ |
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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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streamlit |
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plotly-express |
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pandas |
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numpy |
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scipy |
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Equation |
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#!/bin/bash |
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echo -e "INFO:\tThis executable is a replacement for invoking \`streamlit\`; it will attempt to first launch a docker image \`streamlit:latest\` and if it cannot find \`docker\` then it will attempt to invoke \`streamlit\` directly (you will need to run \`pip install -r requirements.txt\` for it to work)\n\n" |
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IMAGE_NAME=streamlit:latest |
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COMMAND="streamlit" |
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OPTS="--browser.serverAddress 0.0.0.0 --server.enableCORS False --server.enableXsrfProtection False" |
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if ! command -v docker &> /dev/null |
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then |
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echo -e "WARNING:\tdocker could not be found, attempting running locally...\n" |
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$COMMAND $@ $OPTS |
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else |
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echo -e "INFO:\t mounting \`pwd\` into container at mountpoint (and working directory) \`/tmp\` so that latest version of app & state are reflected.\n" |
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docker run --name streamlit --rm -d -p 8501:8501 -v "$(pwd)":/tmp -w /tmp "$IMAGE_NAME" "$COMMAND" $@ $OPTS |
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fi |
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