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import streamlit as st
import pandas as pd
from utils import call_subprocess
def insert_arrow():
return '<svg xmlns="http://www.w3.org/2000/svg" style="width:25px;" fill="none" viewBox="0 0 24 24" stroke-width="1.5" stroke="currentColor" class="w-6 h-6"><path stroke-linecap="round" stroke-linejoin="round" d="M17.25 8.25L21 12m0 0l-3.75 3.75M21 12H3" /></svg>'
def insert_chart_icon(choice=0):
if choice == 0:
return '<svg xmlns="http://www.w3.org/2000/svg" style="width:25px;" viewBox="0 0 20 20" fill="currentColor" class="w-5 h-5"><path fill-rule="evenodd" d="M3 3.5A1.5 1.5 0 014.5 2h6.879a1.5 1.5 0 011.06.44l4.122 4.12A1.5 1.5 0 0117 7.622V16.5a1.5 1.5 0 01-1.5 1.5h-11A1.5 1.5 0 013 16.5v-13zM13.25 9a.75.75 0 01.75.75v4.5a.75.75 0 01-1.5 0v-4.5a.75.75 0 01.75-.75zm-6.5 4a.75.75 0 01.75.75v.5a.75.75 0 01-1.5 0v-.5a.75.75 0 01.75-.75zm4-1.25a.75.75 0 00-1.5 0v2.5a.75.75 0 001.5 0v-2.5z" clip-rule="evenodd" /></svg>'
else:
return '<svg xmlns="http://www.w3.org/2000/svg" style="width:25px;" viewBox="0 0 20 20" fill="currentColor" class="w-5 h-5"><path fill-rule="evenodd" d="M4.5 2A1.5 1.5 0 003 3.5v13A1.5 1.5 0 004.5 18h11a1.5 1.5 0 001.5-1.5V7.621a1.5 1.5 0 00-.44-1.06l-4.12-4.122A1.5 1.5 0 0011.378 2H4.5zm2.25 8.5a.75.75 0 000 1.5h6.5a.75.75 0 000-1.5h-6.5zm0 3a.75.75 0 000 1.5h6.5a.75.75 0 000-1.5h-6.5z" clip-rule="evenodd" /></svg>'
def insert_edit_icon():
return '<svg xmlns="http://www.w3.org/2000/svg" style="width:25px;" viewBox="0 0 20 20" fill="currentColor" class="w-5 h-5"><path d="M5.433 13.917l1.262-3.155A4 4 0 017.58 9.42l6.92-6.918a2.121 2.121 0 013 3l-6.92 6.918c-.383.383-.84.685-1.343.886l-3.154 1.262a.5.5 0 01-.65-.65z" /><path d="M3.5 5.75c0-.69.56-1.25 1.25-1.25H10A.75.75 0 0010 3H4.75A2.75 2.75 0 002 5.75v9.5A2.75 2.75 0 004.75 18h9.5A2.75 2.75 0 0017 15.25V10a.75.75 0 00-1.5 0v5.25c0 .69-.56 1.25-1.25 1.25h-9.5c-.69 0-1.25-.56-1.25-1.25v-9.5z" /></svg>'
def insert_generate_icon():
return '<svg xmlns="http://www.w3.org/2000/svg" style="width:25px;" viewBox="0 0 20 20" fill="currentColor" class="w-5 h-5"><path fill-rule="evenodd" d="M10 1a.75.75 0 01.75.75v1.5a.75.75 0 01-1.5 0v-1.5A.75.75 0 0110 1zM5.05 3.05a.75.75 0 011.06 0l1.062 1.06A.75.75 0 116.11 5.173L5.05 4.11a.75.75 0 010-1.06zm9.9 0a.75.75 0 010 1.06l-1.06 1.062a.75.75 0 01-1.062-1.061l1.061-1.06a.75.75 0 011.06 0zM3 8a.75.75 0 01.75-.75h1.5a.75.75 0 010 1.5h-1.5A.75.75 0 013 8zm11 0a.75.75 0 01.75-.75h1.5a.75.75 0 010 1.5h-1.5A.75.75 0 0114 8zm-6.828 2.828a.75.75 0 010 1.061L6.11 12.95a.75.75 0 01-1.06-1.06l1.06-1.06a.75.75 0 011.06 0zm3.594-3.317a.75.75 0 00-1.37.364l-.492 6.861a.75.75 0 001.204.65l1.043-.799.985 3.678a.75.75 0 001.45-.388l-.978-3.646 1.292.204a.75.75 0 00.74-1.16l-3.874-5.764z" clip-rule="evenodd" /></svg>'
@st.cache_resource
def get_cvms_version():
return (
str(
call_subprocess(
f"Rscript cvms_version.R",
message="cvms versioning script",
return_output=True,
encoding="UTF-8",
)
)
.split("[1]")[-1]
.replace("‘", "")
.replace("’", "")
)
@st.cache_data
def get_example_counts():
return pd.DataFrame(
{
"Target": ["cl1", "cl2", "cl1", "cl2"],
"Prediction": ["cl1", "cl2", "cl2", "cl1"],
"Sub*": [
"(57/60)",
"(46/50)",
"(12/15)",
"(23/25)",
],
"N": [12, 10, 3, 5],
}
)
@st.cache_data
def get_example_data():
return pd.DataFrame(
{
"Target": ["cl1", "cl1", "cl2", "cl2", "cl1", "cl1"],
"Prediction": ["cl1", "cl2", "cl2", "cl1", "cl1", "cl2"],
}
)
def intro_text():
col1, col2 = st.columns([8, 2])
with col1:
st.title("Plot Confusion Matrix")
st.markdown(
"A confusion matrix plot is a great tool for inspecting your "
"machine learning model's performance on a classification task. "
"This application enables you to plot a confusion matrix on your own data, "
"**without a single line of code**. \n\n"
"It's designed for high flexibility AND quick results with good default settings.\n\n"
)
with col2:
st.image(
"https://github.com/LudvigOlsen/cvms/raw/master/man/figures/cvms_logo_242x280_250dpi.png",
width=125,
)
st.markdown("""---""")
col1, col2 = st.columns(2)
with col1:
st.subheader("Have your data ready?")
st.markdown( # TODO: Make A,B, etc. icons
"Upload a csv file with either: \n\n"
f"{insert_chart_icon(1)} **Targets** and **predictions** \n\n"
f"{insert_chart_icon(0)} Existing confusion matrix **counts** \n\n"
f"{insert_arrow()} Specify the columns to use\n\n"
f"{insert_arrow()} Press **Generate plot**\n\n",
unsafe_allow_html=True,
)
with col2:
st.subheader("No data to upload?")
st.markdown(
"No worries! Either: \n\n"
f"{insert_edit_icon()} **Input** your counts directly! \n\n"
f"{insert_generate_icon()} **Generate** some data with **very** easy controls! \n\n"
f"{insert_arrow()} Press **Generate plot**\n\n",
unsafe_allow_html=True,
)
st.markdown("""---""")
st.markdown(
"This release is an **alpha** version (very early). Report errors or suggestions "
"on [GitHub](https://github.com/LudvigOlsen/cvms_plot_app/issues)."
)
st.write(
"The plot is created with the [**cvms**](https://github.com/LudvigOlsen/cvms) R package "
f"(v/{get_cvms_version()}, LR Olsen & HB Zachariae, 2019)."
) # TODO Add citation stuff
st.markdown(
'<p class="small-font">'
"DATA PRIVACY: For technical reasons, the uploaded data is temporarily stored "
"on the server. While we, the authors, won't access your data, we make "
"*no guarantees* about the privacy of your data (not our servers). "
"Please do not upload sensitive data. The application "
"only requires either predictions and targets or counts. "
"</p>",
unsafe_allow_html=True,
)
def generate_data_text():
st.subheader("Generate data")
st.write(
"Quickly try the application by generating a dataset with targets and predictions. "
"Select a number of classes and observations, and you're ready to go! "
)
def enter_count_data_text():
st.subheader("Enter counts")
st.write(
"If you already have the confusion matrix counts and want to plot them. "
"Enter the counts and get designing! "
)
st.write("Start by entering the names of your classes:")
def upload_counts_text():
st.subheader("Upload your counts")
st.write(
"Plot an existing confusion matrix (counts of target-prediction combinations). "
)
col1, col2 = st.columns([5, 4])
with col1:
st.markdown(
"The application expects a `.csv` file with: \n"
"1) A `target classes` column. \n\n"
"2) A `predicted classes` column. \n\n"
"3) A `combination count` column for the "
"combination frequency of 1 and 2. \n\n"
"4) (\\***Optionally**) a `sub` column with text "
"that replaces the bottom text in the middle of tiles. \n\n"
"Other columns are currently ignored. "
"In the next step, you will be asked to select the names of these two columns. "
)
with col2:
st.write("Example of such a file:")
st.dataframe(get_example_counts(), hide_index=True)
def upload_predictions_text():
st.subheader("Upload your predictions")
col1, col2 = st.columns([5, 4])
with col1:
st.markdown(
"The application expects a `.csv` file with: \n"
"1) A `target` column. \n"
"2) A `prediction` column. \n"
"Predictions should be class predictions (not probabilities). \n\n"
"Other columns are currently ignored. \n\n"
"In the next step, you will be asked to select the names of these two columns. "
)
with col2:
st.write("Example of such a file:")
st.dataframe(get_example_data(), hide_index=True)
def columns_text():
st.subheader("Specify columns")
st.write(
"Please select which of the columns in the data should be used for targets and predictions."
)
def design_text():
st.subheader("Design your plot")
st.write("This is where you customize the design of your confusion matrix plot.")
st.markdown(
"We suggest you go directly to `Generate plot` to see the starting point. Then go back and tweak to your liking!"
)
st.markdown(
"The *width* and *height* settings are usually necessary to adjust as they "
"change the relative size of the elements. Try adjusting 100px at a "
"time for a start."
)
st.write(
"If you have previously saved your preferred design settings, "
"you can start by uploading the json file. "
"Otherwise, get designing!"
)
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