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plot_confusion_matrix / app.py
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"""
App for plotting confusion matrix with `cvms::plot_confusion_matrix()`.
"""
import pathlib
import tempfile
from PIL import Image
import streamlit as st # Import last
import pandas as pd
from pandas.api.types import is_float_dtype
from itertools import combinations
from utils import (
call_subprocess,
clean_string_for_non_alphanumerics,
clean_str_column,
min_max_scale_list,
)
from data import read_data, read_data_cached, DownloadHeader, generate_data
from design import design_section
from text_sections import (
intro_text,
columns_text,
upload_predictions_text,
upload_counts_text,
generate_data_text,
enter_count_data_text,
)
st.markdown(
"""
<style>
.small-font {
font-size:0.85em !important;
}
</style>
""",
unsafe_allow_html=True,
)
# Create temporary directory
@st.cache_resource
def set_tmp_dir():
"""
Must cache to avoid regenerating!
Must be the same throughout the iterations!
"""
temp_dir = tempfile.TemporaryDirectory()
return temp_dir, temp_dir.name
temp_dir, temp_dir_path = set_tmp_dir()
gen_data_store_path = pathlib.Path(f"{temp_dir_path}/generated_data.csv")
data_store_path = pathlib.Path(f"{temp_dir_path}/data.csv")
design_settings_store_path = pathlib.Path(f"{temp_dir_path}/design_settings.json")
conf_mat_path = pathlib.Path(f"{temp_dir_path}/confusion_matrix.png")
def input_choice_callback():
"""
Resets steps to 0.
Used when switching between input methods.
"""
st.session_state["step"] = 0
st.session_state["input_type"] = None
st.session_state["num_resets"] = 0
to_delete = ["classes", "count_data", "uploaded_design_settings"]
for key in to_delete:
if key in st.session_state:
st.session_state.pop(key)
# Remove old tmp files
if gen_data_store_path.exists():
gen_data_store_path.unlink()
if data_store_path.exists():
data_store_path.unlink()
if conf_mat_path.exists():
conf_mat_path.unlink()
# Allows design settings to show
st.session_state["design_reset_mode"] = False
# Text
intro_text()
# Start step counter
# Required to make dependent forms work
if st.session_state.get("step") is None:
st.session_state["step"] = 0
input_choice = st.radio(
label="Input Choice",
label_visibility="hidden",
key="InputChoice",
options=["Upload predictions", "Upload counts", "Generate", "Enter counts"],
index=0,
horizontal=True,
on_change=input_choice_callback,
)
if st.session_state.get("input_type") is None:
if input_choice in ["Upload predictions", "Generate"]:
st.session_state["input_type"] = "data"
else:
st.session_state["input_type"] = "counts"
# Load data
if input_choice == "Upload predictions":
with st.form(key="data_form"):
upload_predictions_text()
data_path = st.file_uploader("Upload a dataset", type=["csv"])
if st.form_submit_button(label="Use data"):
if data_path:
st.session_state["step"] = 1
else:
st.session_state["step"] = 0
st.markdown(
"Please upload a file first (or **generate** some random data to try the function)."
)
if st.session_state["step"] >= 1:
# Read and store (tmp) data
df = read_data_cached(data_path)
with st.form(key="column_form"):
columns_text()
target_col = st.selectbox("Targets column", options=list(df.columns))
prediction_col = st.selectbox(
"Predictions column", options=list(df.columns)
)
if st.form_submit_button(label="Set columns"):
st.session_state["step"] = 2
# Load data
elif input_choice == "Upload counts":
with st.form(key="data_form"):
upload_counts_text()
data_path = st.file_uploader("Upload your counts", type=["csv"])
if st.form_submit_button(label="Use counts"):
if data_path:
st.session_state["step"] = 1
else:
st.session_state["step"] = 0
st.write("Please upload a file first.")
if st.session_state["step"] >= 1:
# Read and store (tmp) data
st.session_state["count_data"] = read_data_cached(data_path)
with st.form(key="column_form"):
columns_text()
target_col = st.selectbox(
"Targets column", options=list(st.session_state["count_data"].columns)
)
prediction_col = st.selectbox(
"Predictions column",
options=list(st.session_state["count_data"].columns),
)
n_col = st.selectbox(
"Counts column", options=list(st.session_state["count_data"].columns)
)
sub_col = st.selectbox(
"Sub column",
options=["--"] + list(st.session_state["count_data"].columns),
help="Optional! This column will replace the bottom text in the middle of the tiles.",
)
if st.form_submit_button(label="Set columns"):
st.session_state["step"] = 2
if st.session_state["step"] >= 2:
# Ensure targets and predictions are clean strings
st.session_state["count_data"][target_col] = clean_str_column(
st.session_state["count_data"][target_col]
)
st.session_state["count_data"][prediction_col] = clean_str_column(
st.session_state["count_data"][prediction_col]
)
st.session_state["classes"] = sorted(
[c for c in st.session_state["count_data"][target_col].unique()]
)
# Generate data
elif input_choice == "Generate":
def reset_generation_callback():
p = pathlib.Path(gen_data_store_path)
if p.exists():
p.unlink()
with st.form(key="generate_form"):
generate_data_text()
col1, col2, col3 = st.columns(3)
with col1:
num_classes = st.number_input(
"# Classes",
value=3,
min_value=2,
help="Number of classes to generate data for.",
)
with col2:
num_observations = st.number_input(
"# Observations",
value=30,
min_value=2,
max_value=10000,
help="Number of observations to generate data for.",
)
with col3:
seed = st.number_input("Random Seed", value=42, min_value=0)
if st.form_submit_button(
label="Generate data", on_click=reset_generation_callback
):
st.session_state["step"] = 2
if st.session_state["step"] >= 2:
generate_data(
out_path=gen_data_store_path,
num_classes=num_classes,
num_observations=num_observations,
seed=seed,
)
df = read_data(gen_data_store_path)
target_col = "Target"
prediction_col = "Predicted Class"
elif input_choice == "Enter counts":
def repopulate_matrix_callback():
if "count_data" not in st.session_state:
if "count_data" in st.session_state:
st.session_state.pop("count_data")
with st.form(key="enter_classes_form"):
enter_count_data_text()
classes_joined = st.text_input("Classes (comma-separated)")
if st.form_submit_button(
label="Populate matrix", on_click=repopulate_matrix_callback
):
# Extract class names from comma-separated list
# TODO: Allow white space in classes?
st.session_state["classes"] = [
clean_string_for_non_alphanumerics(s) for s in classes_joined.split(",")
]
# Calculate all pairs of predictions and targets
all_pairs = list(combinations(st.session_state["classes"], 2))
all_pairs += [(pair[1], pair[0]) for pair in all_pairs]
all_pairs += [(c, c) for c in st.session_state["classes"]]
# Prepopulate the matrix
st.session_state["count_data"] = pd.DataFrame(
all_pairs, columns=["Target", "Prediction"]
)
st.session_state["count_data"]["Sub"] = ""
st.session_state["count_data"]["N"] = 0
st.session_state["step"] = 1
if st.session_state["step"] >= 1:
with st.form(key="enter_counts_form"):
st.write(
"Fill in the counts by pressing each cell in the `N` column and inputting the counts. "
)
st.markdown(
"(**Optional**) If you wish to specify the bottom text in the middle of the tiles, "
"you can fill in the `Sub` column.",
help="The `sub` column text replaces the bottom text (counts by default). "
"The design settings for the replaced element (e.g. counts) are used for this text instead.",
)
st.info(
"Note: Please click outside the cell before "
"pressing `Generate data` to register your change."
)
new_counts = st.data_editor(
st.session_state["count_data"],
hide_index=True,
column_config={
"Target": st.column_config.TextColumn(disabled=True),
"Prediction": st.column_config.TextColumn(disabled=True),
"Sub": st.column_config.TextColumn(
help="This text replaces the bottom text (in the middle of the tiles). "
"By default, the counts are replaced. "
"Note that the settings for this text are named "
"by the text element it replaces (e.g. **Fonts**>>*Counts*)."
),
"N": st.column_config.NumberColumn(
disabled=False, min_value=0, step=1
),
},
)
if st.form_submit_button(
label="Generate data",
):
st.session_state["count_data"] = new_counts
st.session_state["step"] = 2
if st.session_state["step"] >= 2:
DownloadHeader.header_and_data_download(
"",
data=st.session_state["count_data"],
file_name="confusion_matrix_counts.csv",
label="Download counts",
help="Download counts",
col_sizes=[10, 3],
)
target_col = "Target"
prediction_col = "Prediction"
n_col = "N"
sub_col = "Sub" if any(st.session_state["count_data"]["Sub"]) else None
if st.session_state["step"] >= 2:
data_is_ready = False
if st.session_state["input_type"] == "data":
# Remove unused columns
df = df.loc[:, [target_col, prediction_col]]
predictions_are_probabilities = is_float_dtype(df[prediction_col])
if predictions_are_probabilities:
st.error(
"Predictions should be the predicted classes - not probabilities. "
)
data_is_ready = False
else:
data_is_ready = True
if data_is_ready:
# Ensure targets and predictions are clean strings
df[target_col] = clean_str_column(df[target_col])
df[prediction_col] = clean_str_column(df[prediction_col])
# Save to tmp directory to allow reading in R script
df.to_csv(data_store_path, index=False)
# Extract unique classes
st.session_state["classes"] = sorted(
[str(c) for c in df[target_col].unique()]
)
st.subheader("The data")
col1, col2, col3 = st.columns([3, 2, 3])
with col2:
st.dataframe(df.head(5), hide_index=True)
st.write(f"{df.shape} (Showing first 5 rows)")
else:
count_data_clean = st.session_state["count_data"].copy()
if not any(count_data_clean["Sub"]):
del count_data_clean["Sub"]
count_data_clean.to_csv(data_store_path, index=False)
data_is_ready = True
if data_is_ready:
# Check the number of classes
num_classes = len(st.session_state["classes"])
if num_classes < 2:
# TODO Handle better than throwing error?
raise ValueError(
"Uploaded data must contain 2 or more classes in `Targets column`. "
f"Got {num_classes} target classes."
)
# Section for specifying design settings
design_ready, selected_classes = design_section(
num_classes=num_classes,
design_settings_store_path=design_settings_store_path,
)
# design_ready tells us whether to proceed or wait
# for user to fix issues
if st.session_state["step"] >= 3 and design_ready:
DownloadHeader.centered_json_download(
data=st.session_state["selected_design_settings"],
file_name="design_settings.json",
label="Download design settings (*Generate first!*)",
help="Download the design settings to allow reusing settings in future plots. "
"Press `Generate plot` before downloading to include all the latest design changes.",
)
st.markdown("---")
selected_classes_string = ",".join([f"'{c}'" for c in selected_classes])
plotting_args = [
"--data_path",
f"'{data_store_path}'",
"--out_path",
f"'{conf_mat_path}'",
"--settings_path",
f"'{design_settings_store_path}'",
"--target_col",
f"'{target_col}'",
"--prediction_col",
f"'{prediction_col}'",
"--classes",
f"{selected_classes_string}",
]
if "sub_col" in locals() and sub_col is not None and sub_col != "--":
plotting_args += ["--sub_col", f"{sub_col}"]
if st.session_state["input_type"] == "counts":
# The input data are counts
plotting_args += ["--n_col", f"{n_col}", "--data_are_counts"]
plotting_args = " ".join(plotting_args)
call_subprocess(
f"Rscript plot.R {plotting_args}",
message="Plotting script",
return_output=True,
encoding="UTF-8",
)
(
image_col_size,
st.session_state["show_greyscale"],
) = DownloadHeader.slider_and_image_download(
filepath=conf_mat_path,
download_label="Download plot",
slider_label="Zoom",
toggle_label="Show greyscale",
toggle_value=True,
toggle_cols=[10, 1],
slider_help="Zoom in/out to better match the size you expect to have in a paper etc. "
"This affects the font sizes and will likely lead to adjustments of `height` and `width`.",
)
st.session_state["image_col_size"] = (
min_max_scale_list(
x=[image_col_size],
new_min=2.0,
new_max=8.0,
old_min=0.0,
old_max=1.0,
)[0]
if image_col_size <= 1
else min_max_scale_list(
x=[image_col_size],
new_min=8.0,
new_max=23.0,
old_min=1.0,
old_max=2.0,
)[0]
)
col1, col2, col3 = st.columns([2, st.session_state["image_col_size"], 2])
with col2:
st.write(" ")
st.write(" ")
image = Image.open(str(conf_mat_path)[:-3] + "jpg")
st.image(
image,
caption="Confusion Matrix",
clamp=False,
channels="RGB",
output_format="auto",
)
if st.session_state["show_greyscale"]:
# Convert the image to grayscale
st.write(" ")
image = image.convert("CMYK").convert("L")
st.image(
image,
caption="Greyscale version for assessing colors in print",
clamp=False,
channels="RGB",
output_format="auto",
)
st.write(" ")
st.write("Note: The downloadable file has a transparent background.")
else:
st.write("Please upload data.")
# Spacing
for _ in range(5):
st.write(" ")
st.markdown("---")
st.write()
col1, col2, col3, _ = st.columns([6, 3, 3, 3])
with col1:
st.write("Developed by [Ludvig Renbo Olsen](http://ludvigolsen.dk)")
with col2:
st.markdown("[Report issues](https://github.com/LudvigOlsen/cvms_plot_app/issues)")
with col3:
st.markdown("[Source code](https://github.com/LudvigOlsen/cvms_plot_app/)")