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import os
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from src.assets.text_content import SHORT_NAMES
def update_cols(df: pd.DataFrame) -> pd.DataFrame:
'''
Change three header rows to a single header row
Args:
df: Raw dataframe containing 3 separate header rows
Remove this function if the dataframe has only one header row
Returns:
df: Updated dataframe which has only 1 header row instead of 3
'''
default_cols = list(df.columns)
# First 4 columns are initalised in 'update', Append additional columns for games Model, Clemscore, ALL(PLayed) and ALL(Main Score)
update = ['Model', 'Clemscore', 'All(Played)', 'All(Quality Score)']
game_metrics = default_cols[4:]
# Change columns Names for each Game
for i in range(len(game_metrics)):
if i%3 == 0:
game = game_metrics[i]
update.append(str(game).capitalize() + "(Played)")
update.append(str(game).capitalize() + "(Quality Score)")
update.append(str(game).capitalize() + "(Quality Score[std])")
# Create a dict to change names of the columns
map_cols = {}
for i in range(len(default_cols)):
map_cols[default_cols[i]] = str(update[i])
df = df.rename(columns=map_cols)
df = df.iloc[2:]
return df
def process_df(df: pd.DataFrame) -> pd.DataFrame:
'''
Process dataframe - Remove repition in model names, convert datatypes to sort by "float" instead of "str"
Args:
df: Unprocessed Dataframe (after using update_cols)
Returns:
df: Processed Dataframe
'''
# Change column type to float from str
list_column_names = list(df.columns)
model_col_name = list_column_names[0]
for col in list_column_names:
if col != model_col_name:
df[col] = df[col].astype(float)
# Remove repetition in model names, if any
models_list = []
for i in range(len(df)):
model_name = df.iloc[i][model_col_name]
splits = model_name.split('--')
splits = [split.replace('-t0.0', '') for split in splits] # Comment to not remove -t0.0
if splits[0] == splits[1]:
models_list.append(splits[0])
else:
models_list.append(splits[0] + "--" + splits[1])
df[model_col_name] = models_list
return df
def get_data(path: str, flag: bool):
'''
Get a list of all version names and respective Dataframes
Args:
path: Path to the directory containing CSVs of different versions -> v0.9.csv, v1.0.csv, ....
flag: Set this flag to include the latest version in Details and Versions tab
Returns:
latest_df: singular list containing dataframe of the latest version of the leaderboard with only 4 columns
latest_vname: list of the name of latest version
previous_df: list of dataframes for previous versions (can skip latest version if required)
previous_vname: list of the names for the previous versions (INCLUDED IN Details and Versions Tab)
'''
# Check if Directory is empty
list_versions = os.listdir(path)
if not list_versions:
print("Directory is empty")
else:
files = [file for file in list_versions if file.endswith('.csv')]
files.sort(reverse=True)
file_names = [os.path.splitext(file)[0] for file in files]
DFS = []
for file in files:
df = pd.read_csv(os.path.join(path, file))
df = update_cols(df) # Remove if by default there is only one header row
df = process_df(df) # Process Dataframe
df = df.sort_values(by=list(df.columns)[1], ascending=False) # Sort by clemscore
DFS.append(df)
# Only keep relavant columns for the main leaderboard
latest_df_dummy = DFS[0]
all_columns = list(latest_df_dummy.columns)
keep_columns = all_columns[0:4]
latest_df_dummy = latest_df_dummy.drop(columns=[c for c in all_columns if c not in keep_columns])
latest_df = [latest_df_dummy]
latest_vname = [file_names[0]]
previous_df = []
previous_vname = []
for df, name in zip(DFS, file_names):
previous_df.append(df)
previous_vname.append(name)
if not flag:
previous_df.pop(0)
previous_vname.pop(0)
return latest_df, latest_vname, previous_df, previous_vname
return None
# ['Model', 'Clemscore', 'All(Played)', 'All(Quality Score)']
def compare_plots(df: pd.DataFrame, LIST: list):
'''
Quality Score v/s % Played plot by selecting models
Args:
LIST: The list of models to show in the plot, updated from frontend
Returns:
fig: The plot
'''
short_names = label_map(LIST)
list_columns = list(df.columns)
df = df[df[list_columns[0]].isin(LIST)]
X = df[list_columns[2]]
fig, ax = plt.subplots()
for model in LIST:
short = short_names[model][0]
same_flag = short_names[model][1]
model_df = df[df[list_columns[0]] == model]
x = model_df[list_columns[2]]
y = model_df[list_columns[3]]
color = plt.cm.rainbow(x / max(X)) # Use a colormap for different colors
plt.scatter(x, y, color=color)
if same_flag:
plt.annotate(f'{short}', (x, y), textcoords="offset points", xytext=(0, -15), ha='center', rotation=0)
else:
plt.annotate(f'{short}', (x, y), textcoords="offset points", xytext=(20, -3), ha='center', rotation=0)
ax.grid(which='both', color='grey', linewidth=1, linestyle='-', alpha=0.2)
ax.set_xticks(np.arange(0,110,10))
plt.xlim(-10, 110)
plt.ylim(-10, 110)
plt.xlabel('% Played')
plt.ylabel('Quality Score')
plt.title('Overview of benchmark results')
plt.show()
return fig
def label_map(model_list: list) -> dict:
'''
Generate a map from long names to short names, to plot them in frontend graph
Define the short names in src/assets/text_content.py
Args:
model_list: A list of long model names
Returns:
short_name: A map from long to list of short name + indication if models are same or different
'''
short_name = {}
for model_name in model_list:
splits = model_name.split('--')
if len(splits) != 1:
splits[0] = SHORT_NAMES[splits[0] + '-']
splits[1] = SHORT_NAMES[splits[1] + '-']
# Define the short name and indicate there are two different models
short_name[model_name] = [splits[0] + '--' + splits[1], 0]
else:
splits[0] = SHORT_NAMES[splits[0] + '-']
# Define the short name and indicate both models are same
short_name[model_name] = [splits[0], 1]
return short_name
def filter_search(df: pd.DataFrame, query: str) -> pd.DataFrame:
'''
Filter the dataframe based on the search query
Args:
df: Unfiltered dataframe
query: a string of queries separated by ";"
Return:
filtered_df: Dataframe containing searched queries in the 'Model' column
'''
queries = query.split(';')
list_cols = list(df.columns)
df_len = len(df)
filtered_models = []
models_list = list(df[list_cols[0]])
for q in queries:
q = q.lower()
for i in range(df_len):
model_name = models_list[i]
if q in model_name.lower():
filtered_models.append(model_name) # Append model names containing query q
filtered_df = df[df[list_cols[0]].isin(filtered_models)]
if query == "":
return df
return filtered_df
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