diff --git a/ClemBot.Bot/bot/cogs/command_cog.py b/ClemBot.Bot/bot/cogs/command_cog.py index 5e44c7e4..250356c1 100644 --- a/ClemBot.Bot/bot/cogs/command_cog.py +++ b/ClemBot.Bot/bot/cogs/command_cog.py @@ -36,7 +36,7 @@ async def command( model = await self.bot.commands_route.get_details(ctx.guild.id, cmd.qualified_name) assert model is not None - (name, value) = self._disabled_in_field(model) + name, value = self._disabled_in_field(model) embed = discord.Embed(title="⚙️ Command Details", color=Colors.ClemsonOrange) embed.add_field(name="Name", value=f"`{cmd.qualified_name}`") embed.add_field(name="Allows Disabling", value=cmd.allow_disable, inline=False) diff --git a/ClemBot.Bot/bot/cogs/grades_cog/grades_cog.py b/ClemBot.Bot/bot/cogs/grades_cog/grades_cog.py index 6c44c53a..09b33335 100644 --- a/ClemBot.Bot/bot/cogs/grades_cog/grades_cog.py +++ b/ClemBot.Bot/bot/cogs/grades_cog/grades_cog.py @@ -42,6 +42,21 @@ def load_data(self) -> None: for file in os.listdir(ASSET_LOCATION): self.grades_df = pd.concat([self.grades_df, pd.read_csv(f"{ASSET_LOCATION}{file}")]) + # clean some p/f data data + for col in ("P", "F(P)"): + self.grades_df[col] = ( + self.grades_df[col] + .astype(str) + .str.replace("####", "100%", regex=False) # #### means 100% pass + .str.replace("%", "", regex=False) + .pipe(pd.to_numeric, errors="coerce") + .fillna(0) + ) + + self.grades_df.loc[self.grades_df[col] > 1, col] = ( + self.grades_df.loc[self.grades_df[col] > 1, col] / 100 + ) + self.grades_df.info() self.all_profs = self.get_profs() @@ -69,6 +84,61 @@ def fuzzy_find_course(self, course_name: str) -> str | None: if best_match.similarity > 0.3: return best_match.item + LETTER_GRADE_COLS = ["A", "B", "C", "D", "F"] + + @staticmethod + def _split_graded_and_pf( + df: pd.DataFrame, + ) -> tuple[pd.DataFrame, pd.DataFrame]: + # rule for is_pf is if (A-F) == 0 and (P OR F(P)) > 0 + is_pf = (df[GradesCog.LETTER_GRADE_COLS].sum(axis=1) == 0) & ( + (df["P"] > 0) | (df["F(P)"] > 0) + ) + graded_df = df[~is_pf] + pf_df = df[is_pf] + + # drop one random p/f or graded year (e.g. covid semester) + pf_years = pf_df["Year"].nunique() + graded_years = graded_df["Year"].nunique() + + if not pf_df.empty and not graded_df.empty: + if pf_years <= 1 and graded_years > 1: + pf_df = pf_df.iloc[0:0] + elif graded_years <= 1 and pf_years > 1: + graded_df = graded_df.iloc[0:0] + + return graded_df, pf_df + + @staticmethod + def _format_distribution(df: pd.DataFrame) -> str: + graded_df, pf_df = GradesCog._split_graded_and_pf(df) + has_graded = not graded_df.empty + has_pf = not pf_df.empty + + parts = [] + + if has_graded: + mean = graded_df.mean(numeric_only=True) + A = f"{int(mean['A'].round(2) * 100)}%" + B = f"{int(mean['B'].round(2) * 100)}%" + C = f"{int(mean['C'].round(2) * 100)}%" + D = f"{int(mean['D'].round(2) * 100)}%" + F = f"{int(mean['F'].round(2) * 100)}%" + W = f"{int(mean['W'].round(2) * 100)}%" + parts.extend([f"A: {A}", f"B: {B}", f"C: {C}", f"D: {D}", f"F: {F}", f"W: {W}"]) + + if has_pf: + mean = pf_df.mean(numeric_only=True) + P = f"{int(mean['P'].round(2) * 100)}%" + FP = f"{int(mean['F(P)'].round(2) * 100)}%" + if not has_graded: + W = f"{int(mean['W'].round(2) * 100)}%" + parts.extend([f"P: {P}", f"FP: {FP}"]) + if not has_graded: + parts.append(f"W: {W}") + + return "```" + "\n".join(parts) + "```" + @ext.group(invoke_without_command=True, case_insensitive=True) @ext.long_help( """ @@ -151,24 +221,20 @@ async def grades( description = df.Title.tolist()[0] - A = f"{int(df.A.mean().round(2) * 100)}%" - B = f"{int(df.B.mean().round(2) * 100)}%" - C = f"{int(df.C.mean().round(2) * 100)}%" - D = f"{int(df.D.mean().round(2) * 100)}%" - F = f"{int(df.F.mean().round(2) * 100)}%" - W = f"{int(df.W.mean().round(2) * 100)}%" - title = f"Grades for {course} ({honors.title()}) since {year}" embeds = [] + # grouup overall embed = discord.Embed(title=title, color=Colors.ClemsonOrange) embed.set_footer(text=str(ctx.author), icon_url=ctx.author.display_avatar.url) embed.description = description + embed.add_field( name="Overall Distribution", - value=f"```A: {A}\nB: {B}\nC: {C}\nD: {D}\nF: {F}\nW: {W}```", + value=self._format_distribution(df), ) + embed.add_field(name="Total Number of Classes Analyzed", value=str(len(df)), inline=False) embed.add_field( name="Total Number of Professors Found", @@ -182,20 +248,16 @@ async def grades( embeds.append(embed) # group by the prof - - for i, row in df.groupby(["Instructor"]).mean().iterrows(): + for instructor, group in df.groupby("Instructor"): embed = discord.Embed(title=title, color=Colors.ClemsonOrange) embed.description = description embed.set_footer(text=str(ctx.author), icon_url=ctx.author.display_avatar.url) - A = f"{int(row.A.round(2) * 100)}%" - B = f"{int(row.B.round(2) * 100)}%" - C = f"{int(row.C.round(2) * 100)}%" - D = f"{int(row.D.round(2) * 100)}%" - F = f"{int(row.F.round(2) * 100)}%" - W = f"{int(row.W.round(2) * 100)}%" - - val = f"```A: {A}\nB: {B}\nC: {C}\nD: {D}\nF: {F}\nW: {W}```" - embed.add_field(name=f"{row.name}'s distribution", value=val) + + embed.add_field( + name=f"{instructor}'s distribution", + value=self._format_distribution(group), + ) + embed.add_field( name="Total Number of Classes Analyzed", value=str(len(df)), inline=False ) @@ -272,10 +334,7 @@ async def prof( await ctx.send(embed=embed) return - # check for if there is a 0% A rate, that means it was a pass fail class and we dont handle those - df = self.grades_df[ - (self.grades_df.Instructor.str.lower() == prof.lower()) & (self.grades_df.A > 0) - ] + df = self.grades_df[(self.grades_df.Instructor.str.lower() == prof.lower())] # Honors/Non-honors logic if honors == "honors": @@ -299,31 +358,25 @@ async def prof( ) return await ctx.send(embed=embed) - A = f"{int(df.A.mean().round(2) * 100)}%" - B = f"{int(df.B.mean().round(2) * 100)}%" - C = f"{int(df.C.mean().round(2) * 100)}%" - D = f"{int(df.D.mean().round(2) * 100)}%" - F = f"{int(df.F.mean().round(2) * 100)}%" - W = f"{int(df.W.mean().round(2) * 100)}%" - normalized_name = df.Instructor.any() + normalized_name = df.Instructor.iloc[0] - title = f"Overall Grade Distribution for {normalized_name} across all classes taught" + title = f"Grade Distribution for {normalized_name}" if honors == "honors": - title = ( - f"Overall Grade Distribution for {normalized_name} across all honors classes taught" - ) + title += " (Honors)" elif honors == "non-honors": - title = f"Overall Grade Distribution for {normalized_name} across all non-honors classes taught" + title += " (Non-Honors)" embeds = [] embed = discord.Embed(title=title, color=Colors.ClemsonOrange) embed.set_footer(text=str(ctx.author), icon_url=ctx.author.display_avatar.url) + embed.add_field( name="Overall Distribution", - value=f"```A: {A}\nB: {B}\nC: {C}\nD: {D}\nF: {F}\nW: {W}```", + value=self._format_distribution(df), ) + embed.add_field( name="Total Number of Classes Analyzed", value=str(len(df.groupby(["CourseId"]))), @@ -335,25 +388,15 @@ async def prof( ) embeds.append(embed) - title = f"Grade Distribution for {normalized_name}" - - if honors == "honors": - title += " (Honors)" - elif honors == "non-honors": - title += " (Non-Honors)" - - for i, row in df.groupby(["CourseId"]).mean().iterrows(): + for course_id, group in df.groupby("CourseId"): embed = discord.Embed(title=title, color=Colors.ClemsonOrange) embed.set_footer(text=str(ctx.author), icon_url=ctx.author.display_avatar.url) - A = f"{int(row.A.round(2) * 100)}%" - B = f"{int(row.B.round(2) * 100)}%" - C = f"{int(row.C.round(2) * 100)}%" - D = f"{int(row.D.round(2) * 100)}%" - F = f"{int(row.F.round(2) * 100)}%" - W = f"{int(row.W.round(2) * 100)}%" - - val = f"```A: {A}\nB: {B}\nC: {C}\nD: {D}\nF: {F}\nW: {W}```" - embed.add_field(name=f"{row.name}'s distribution", value=val) + + embed.add_field( + name=f"{course_id}'s distribution", + value=self._format_distribution(group), + ) + embed.add_field( name="Total Number of Classes Analyzed", value=str(len(df)), inline=False ) diff --git a/ClemBot.Bot/bot/models/clem_bot_model.py b/ClemBot.Bot/bot/models/clem_bot_model.py index 5945fdbd..4e6ec880 100644 --- a/ClemBot.Bot/bot/models/clem_bot_model.py +++ b/ClemBot.Bot/bot/models/clem_bot_model.py @@ -1,11 +1,12 @@ from humps import camel -from pydantic import BaseModel +from pydantic import BaseModel, ConfigDict class ClemBotModel(BaseModel): """Base model which adds support for camelCase""" - class Config: - alias_generator = camel.case - allow_population_by_field_name = True - use_enum_values = True + model_config = ConfigDict( + alias_generator=camel.case, + populate_by_name=True, + use_enum_values=True, + ) diff --git a/ClemBot.Bot/pyproject.toml b/ClemBot.Bot/pyproject.toml index fb43eed1..2e6e0457 100644 --- a/ClemBot.Bot/pyproject.toml +++ b/ClemBot.Bot/pyproject.toml @@ -13,7 +13,7 @@ dependencies = [ "seqlog>=0.3.20", "pandas>=1.5.1", "humps>=0.2.2", - 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