Feat: import score dataframe functions
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recopytex/datalib/__init__.py
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recopytex/datalib/__init__.py
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recopytex/datalib/on_score_column.py
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recopytex/datalib/on_score_column.py
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#!/usr/bin/env python
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# encoding: utf-8
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from math import ceil
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def score_to_mark(x, score_max, rounding=lambda x: round(x, 2)):
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"""Compute the mark from the score
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if the item is leveled then the score is multiply by the score_rate
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otherwise it copies the score
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:param x: dictionnary with "is_leveled", "score" and "score_rate" keys
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:param score_max:
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:param rounding: rounding mark function
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:return: the mark
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>>> import pandas as pd
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>>> d = {"Eleve":["E1"]*6 + ["E2"]*6,
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... "score_rate":[1]*2+[2]*2+[2]*2 + [1]*2+[2]*2+[2]*2,
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... "is_leveled":[0]*4+[1]*2 + [0]*4+[1]*2,
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... "score":[1, 0.33, 2, 1.5, 1, 3, 0.666, 1, 1.5, 1.2, 2, 3],
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... }
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>>> df = pd.DataFrame(d)
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>>> df.loc[0]
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Eleve E1
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score_rate 1
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is_leveled 0
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score 1.0
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Name: 0, dtype: object
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>>> score_to_mark(df.loc[0], 3)
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1.0
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>>> df.loc[10]
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Eleve E2
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score_rate 2
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is_leveled 1
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score 2.0
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Name: 10, dtype: object
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>>> score_to_mark(df.loc[10], 3)
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1.33
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>>> from .on_value import round_half_point
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>>> score_to_mark(df.loc[10], 3, round_half_point)
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1.5
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>>> df.loc[1]
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Eleve E1
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score_rate 1
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is_leveled 0
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score 0.33
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Name: 1, dtype: object
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>>> score_to_mark(df.loc[1], 3)
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0.33
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"""
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if x["is_leveled"]:
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if x["score"] not in list(range(score_max + 1)):
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raise ValueError(f"The evaluation is out of range: {x['score']} at {x}")
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return rounding(x["score"] * x["score_rate"] / score_max)
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return rounding(x["score"])
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def score_to_level(x, level_max=3):
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"""Compute the level (".",0,1,2,3).
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:param x: dictionnary with "is_leveled", "score" and "score_rate" keys
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:return: the level
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>>> import pandas as pd
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>>> d = {"Eleve":["E1"]*6 + ["E2"]*6,
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... "score_rate":[1]*2+[2]*2+[2]*2 + [1]*2+[2]*2+[2]*2,
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... "is_leveled":[0]*4+[1]*2 + [0]*4+[1]*2,
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... "score":[1, 0.33, 0, 1.5, 1, 3, 0.666, 1, 1.5, 1, 2, 3],
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... }
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>>> df = pd.DataFrame(d)
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>>> df
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Eleve score_rate is_leveled score
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0 E1 1 0 1.000
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1 E1 1 0 0.330
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2 E1 2 0 0.000
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3 E1 2 0 1.500
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4 E1 2 1 1.000
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5 E1 2 1 3.000
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6 E2 1 0 0.666
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7 E2 1 0 1.000
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8 E2 2 0 1.500
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9 E2 2 0 1.000
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10 E2 2 1 2.000
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11 E2 2 1 3.000
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>>> df.apply(score_to_level, axis=1)
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0 3
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1 1
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2 0
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3 3
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4 1
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5 3
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6 2
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7 3
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8 3
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9 2
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10 2
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11 3
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dtype: int64
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>>> df.apply(lambda x: score_to_level(x, 5), axis=1)
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0 5
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1 2
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2 0
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3 4
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4 1
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5 3
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6 4
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7 5
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8 4
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9 3
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10 2
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11 3
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dtype: int64
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"""
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if x["is_leveled"]:
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return int(x["score"])
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if x["score"] > x["score_rate"]:
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raise ValueError(
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f"score is higher than score_rate ({x['score']} > {x['score_rate']}) for {x}"
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)
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return int(ceil(x["score"] / x["score_rate"] * level_max))
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# -----------------------------
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# Reglages pour 'vim'
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# vim:set autoindent expandtab tabstop=4 shiftwidth=4:
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# cursor: 16 del
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recopytex/datalib/on_score_dataframe.py
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recopytex/datalib/on_score_dataframe.py
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#!/usr/bin/env python
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# encoding: utf-8
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from .on_score_column import score_to_mark, score_to_level
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def compute_marks(df, score_max, rounding=lambda x: round(x, 2)):
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"""Compute the mark for the dataframe
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apply score_to_mark to each row
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:param df: DataFrame with "score", "is_leveled" and "score_rate" columns.
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>>> import pandas as pd
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>>> d = {"Eleve":["E1"]*6 + ["E2"]*6,
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... "score_rate":[1]*2+[2]*2+[2]*2 + [1]*2+[2]*2+[2]*2,
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... "is_leveled":[0]*4+[1]*2 + [0]*4+[1]*2,
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... "score":[1, 0.33, 2, 1.5, 1, 3, 0.666, 1, 1.5, 1, 2, 3],
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... }
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>>> df = pd.DataFrame(d)
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>>> df
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Eleve score_rate is_leveled score
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0 E1 1 0 1.000
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1 E1 1 0 0.330
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2 E1 2 0 2.000
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3 E1 2 0 1.500
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4 E1 2 1 1.000
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5 E1 2 1 3.000
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6 E2 1 0 0.666
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7 E2 1 0 1.000
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8 E2 2 0 1.500
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9 E2 2 0 1.000
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10 E2 2 1 2.000
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11 E2 2 1 3.000
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>>> compute_marks(df, 3)
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0 1.00
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1 0.33
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2 2.00
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3 1.50
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4 0.67
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5 2.00
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6 0.67
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7 1.00
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8 1.50
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9 1.00
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10 1.33
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11 2.00
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dtype: float64
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>>> from .on_value import round_half_point
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>>> compute_marks(df, 3, round_half_point)
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0 1.0
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1 0.5
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2 2.0
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3 1.5
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4 0.5
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5 2.0
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6 0.5
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7 1.0
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8 1.5
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9 1.0
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10 1.5
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11 2.0
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dtype: float64
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"""
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return df[["score", "is_leveled", "score_rate"]].apply(
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lambda x: score_to_mark(x, score_max, rounding), axis=1
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)
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def compute_level(df, level_max=3):
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"""Compute level for the dataframe
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Applies score_to_level to each row
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:param df: DataFrame with "score", "is_leveled" and "score_rate" columns.
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:return: Columns with level
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>>> import pandas as pd
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>>> import numpy as np
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>>> d = {"Eleve":["E1"]*6 + ["E2"]*6,
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... "score_rate":[1]*2+[2]*2+[2]*2 + [1]*2+[2]*2+[2]*2,
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... "is_leveled":[0]*4+[1]*2 + [0]*4+[1]*2,
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... "score":[0, 0.33, 2, 1.5, 1, 3, 0.666, 1, 1.5, 1, 2, 3],
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... }
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>>> df = pd.DataFrame(d)
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>>> compute_level(df)
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0 0
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1 1
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2 3
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3 3
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4 1
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5 3
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6 2
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7 3
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8 3
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9 2
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10 2
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11 3
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dtype: int64
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"""
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return df[["score", "is_leveled", "score_rate"]].apply(
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lambda x: score_to_level(x, level_max), axis=1
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)
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def compute_normalized(df, rounding=lambda x: round(x, 2)):
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"""Compute the normalized mark (Mark / score_rate)
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:param df: DataFrame with "Mark" and "score_rate" columns
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:return: column with normalized mark
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>>> import pandas as pd
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>>> d = {"Eleve":["E1"]*6 + ["E2"]*6,
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... "score_rate":[1]*2+[2]*2+[2]*2 + [1]*2+[2]*2+[2]*2,
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... "is_leveled":[0]*4+[1]*2 + [0]*4+[1]*2,
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... "score":[1, 0.33, 2, 1.5, 1, 3, 0.666, 1, 1.5, 1, 2, 3],
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... }
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>>> df = pd.DataFrame(d)
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>>> df["mark"] = compute_marks(df, 3)
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>>> compute_normalized(df)
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0 1.00
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1 0.33
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2 1.00
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3 0.75
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4 0.34
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5 1.00
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6 0.67
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7 1.00
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8 0.75
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9 0.50
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10 0.66
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11 1.00
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dtype: float64
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"""
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return rounding(df["mark"] / df["score_rate"])
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# -----------------------------
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# Reglages pour 'vim'
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# vim:set autoindent expandtab tabstop=4 shiftwidth=4:
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# cursor: 16 del
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40
recopytex/datalib/on_value.py
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recopytex/datalib/on_value.py
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#!/usr/bin/env python
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# encoding: utf-8
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from math import ceil, floor
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def round_with_base(x, base=0.5):
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"""Round to a multiple of base
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:example:
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>>> round_with_base(1.33, 0.1)
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1.3
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>>> round_with_base(1.33, 0.2)
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1.4
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>>> round_with_base(1.33, 1)
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1
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>>> round_with_base(1.33, 2)
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2
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"""
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try:
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prec = len(str(base).split(".")[1])
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except IndexError:
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prec = 0
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return round(base * round(float(x) / base), prec)
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def round_half_point(x):
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"""Round to nearest half point
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:example:
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>>> round_half_point(1.33)
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1.5
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>>> round_half_point(1.1)
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1.0
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>>> round_half_point(1.66)
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1.5
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>>> round_half_point(1.76)
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2.0
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"""
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return round_with_base(x, base=0.5)
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