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Author SHA1 Message Date
Bertrand Benjamin 2e86b3a0a2 Feat: filter none numeric scores 2021-04-19 14:30:37 +02:00
Bertrand Benjamin 7e6b24aaea Feat: rename valid_scores to scores in config 2021-04-19 14:29:57 +02:00
4 changed files with 73 additions and 7 deletions
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9
recopytex/database/filesystem/default_config.yml
View File

@ -21,27 +21,34 @@ competences: # Competences
name: Communiquer
abrv: Com
valid_scores: #
scores: #
BAD: # Everything is bad
value: 0
numeric_value: 0
color: "#E7472B"
FEW: # Few good things
value: 1
numeric_value: 1
color: "#FF712B"
NEARLY: # Nearly good but things are missing
value: 2
numeric_value: 2
color: "#F2EC4C"
GOOD: # Everything is good
value: 3
numeric_value: 3
color: "#68D42F"
NOTFILLED: # The item is not scored yet
value: ""
numeric_value: None
color: white
NOANSWER: # Student gives no answer (count as 0)
value: "."
numeric_value: 0
color: black
ABS: # Student has absent (this score won't be impact the final mark)
value: a
numeric_value: None
color: lightgray
csv_fields: # dataframe_field: csv_field

4
recopytex/database/filesystem/loader.py
View File

@ -31,10 +31,10 @@ class CSVLoader(Loader):
:example:
>>> loader = CSVLoader()
>>> loader.get_config()
{'source': './', 'competences': {'Chercher': {'name': 'Chercher', 'abrv': 'Cher'}, 'Représenter': {'name': 'Représenter', 'abrv': 'Rep'}, 'Modéliser': {'name': 'Modéliser', 'abrv': 'Mod'}, 'Raisonner': {'name': 'Raisonner', 'abrv': 'Rai'}, 'Calculer': {'name': 'Calculer', 'abrv': 'Cal'}, 'Communiquer': {'name': 'Communiquer', 'abrv': 'Com'}}, 'valid_scores': {'BAD': {'value': 0, 'color': '#E7472B'}, 'FEW': {'value': 1, 'color': '#FF712B'}, 'NEARLY': {'value': 2, 'color': '#F2EC4C'}, 'GOOD': {'value': 3, 'color': '#68D42F'}, 'NOTFILLED': {'value': '', 'color': 'white'}, 'NOANSWER': {'value': '.', 'color': 'black'}, 'ABS': {'value': 'a', 'color': 'lightgray'}}, 'csv_fields': {'term': 'Trimestre', 'exam': 'Nom', 'date': 'Date', 'exercise': 'Exercice', 'question': 'Question', 'competence': 'Competence', 'theme': 'Domaine', 'comment': 'Commentaire', 'score_rate': 'Bareme', 'is_leveled': 'Est_nivele'}, 'id_templates': {'exam': '{name}_{tribe}', 'question': '{exam_id}_{exercise}_{question}_{comment}'}}
{'source': './', 'competences': {'Chercher': {'name': 'Chercher', 'abrv': 'Cher'}, 'Représenter': {'name': 'Représenter', 'abrv': 'Rep'}, 'Modéliser': {'name': 'Modéliser', 'abrv': 'Mod'}, 'Raisonner': {'name': 'Raisonner', 'abrv': 'Rai'}, 'Calculer': {'name': 'Calculer', 'abrv': 'Cal'}, 'Communiquer': {'name': 'Communiquer', 'abrv': 'Com'}}, 'scores': {'BAD': {'value': 0, 'numeric_value': 0, 'color': '#E7472B'}, 'FEW': {'value': 1, 'numeric_value': 1, 'color': '#FF712B'}, 'NEARLY': {'value': 2, 'numeric_value': 2, 'color': '#F2EC4C'}, 'GOOD': {'value': 3, 'numeric_value': 3, 'color': '#68D42F'}, 'NOTFILLED': {'value': '', 'numeric_value': 'None', 'color': 'white'}, 'NOANSWER': {'value': '.', 'numeric_value': 0, 'color': 'black'}, 'ABS': {'value': 'a', 'numeric_value': 'None', 'color': 'lightgray'}}, 'csv_fields': {'term': 'Trimestre', 'exam': 'Nom', 'date': 'Date', 'exercise': 'Exercice', 'question': 'Question', 'competence': 'Competence', 'theme': 'Domaine', 'comment': 'Commentaire', 'score_rate': 'Bareme', 'is_leveled': 'Est_nivele'}, 'id_templates': {'exam': '{name}_{tribe}', 'question': '{exam_id}_{exercise}_{question}_{comment}'}}
>>> loader = CSVLoader("./test_config.yml")
>>> loader.get_config()
{'source': './example', 'competences': {'Chercher': {'name': 'Chercher', 'abrv': 'Cher'}, 'Représenter': {'name': 'Représenter', 'abrv': 'Rep'}, 'Modéliser': {'name': 'Modéliser', 'abrv': 'Mod'}, 'Raisonner': {'name': 'Raisonner', 'abrv': 'Rai'}, 'Calculer': {'name': 'Calculer', 'abrv': 'Cal'}, 'Communiquer': {'name': 'Communiquer', 'abrv': 'Com'}}, 'valid_scores': {'BAD': {'value': 0, 'color': '#E7472B'}, 'FEW': {'value': 1, 'color': '#FF712B'}, 'NEARLY': {'value': 2, 'color': '#F2EC4C'}, 'GOOD': {'value': 3, 'color': '#68D42F'}, 'NOTFILLED': {'value': '', 'color': 'white'}, 'NOANSWER': {'value': '.', 'color': 'black'}, 'ABS': {'value': 'a', 'color': 'lightgray'}}, 'csv_fields': {'term': 'Trimestre', 'exam': 'Nom', 'date': 'Date', 'exercise': 'Exercice', 'question': 'Question', 'competence': 'Competence', 'theme': 'Domaine', 'comment': 'Commentaire', 'score_rate': 'Bareme', 'is_leveled': 'Est_nivele'}, 'id_templates': {'exam': '{name}_{tribe}', 'question': '{exam_id}_{exercise}_{question}_{comment}'}, 'output': './output', 'templates': 'templates/', 'tribes': {'Tribe1': {'name': 'Tribe1', 'type': 'Type1', 'students': 'tribe1.csv'}, 'Tribe2': {'name': 'Tribe2', 'students': 'tribe2.csv'}}}
{'source': './example', 'competences': {'Chercher': {'name': 'Chercher', 'abrv': 'Cher'}, 'Représenter': {'name': 'Représenter', 'abrv': 'Rep'}, 'Modéliser': {'name': 'Modéliser', 'abrv': 'Mod'}, 'Raisonner': {'name': 'Raisonner', 'abrv': 'Rai'}, 'Calculer': {'name': 'Calculer', 'abrv': 'Cal'}, 'Communiquer': {'name': 'Communiquer', 'abrv': 'Com'}}, 'scores': {'BAD': {'value': 0, 'numeric_value': 0, 'color': '#E7472B'}, 'FEW': {'value': 1, 'numeric_value': 1, 'color': '#FF712B'}, 'NEARLY': {'value': 2, 'numeric_value': 2, 'color': '#F2EC4C'}, 'GOOD': {'value': 3, 'numeric_value': 3, 'color': '#68D42F'}, 'NOTFILLED': {'value': '', 'numeric_value': 'None', 'color': 'white'}, 'NOANSWER': {'value': '.', 'numeric_value': 0, 'color': 'black'}, 'ABS': {'value': 'a', 'numeric_value': 'None', 'color': 'lightgray'}}, 'csv_fields': {'term': 'Trimestre', 'exam': 'Nom', 'date': 'Date', 'exercise': 'Exercice', 'question': 'Question', 'competence': 'Competence', 'theme': 'Domaine', 'comment': 'Commentaire', 'score_rate': 'Bareme', 'is_leveled': 'Est_nivele'}, 'id_templates': {'exam': '{name}_{tribe}', 'question': '{exam_id}_{exercise}_{question}_{comment}'}, 'output': './output', 'templates': 'templates/', 'tribes': {'Tribe1': {'name': 'Tribe1', 'type': 'Type1', 'students': 'tribe1.csv'}, 'Tribe2': {'name': 'Tribe2', 'students': 'tribe2.csv'}}}
"""
CONFIG = DEFAULT_CONFIG

6
recopytex/datalib/on_score_column.py
View File

@ -10,7 +10,7 @@ def score_to_mark(x, score_max, rounding=lambda x: round(x, 2)):
if the item is leveled then the score is multiply by the score_rate
otherwise it copies the score
:param x: dictionnary with "is_leveled", "score" and "score_rate" keys
:param x: dictionnary with "is_leveled", "score" (need to be number) and "score_rate" keys
:param score_max:
:param rounding: rounding mark function
:return: the mark
@ -125,6 +125,10 @@ def score_to_level(x, level_max=3):
return int(ceil(x["score"] / x["score_rate"] * level_max))
def score_to_numeric_score(x, score_config):
pass
# -----------------------------
# Reglages pour 'vim'
# vim:set autoindent expandtab tabstop=4 shiftwidth=4:

61
recopytex/datalib/on_score_dataframe.py
View File

@ -2,6 +2,7 @@
# encoding: utf-8
from .on_score_column import score_to_mark, score_to_level
import pandas as pd
def compute_marks(df, score_max, rounding=lambda x: round(x, 2)):
@ -9,7 +10,7 @@ def compute_marks(df, score_max, rounding=lambda x: round(x, 2)):
apply score_to_mark to each row
:param df: DataFrame with "score", "is_leveled" and "score_rate" columns.
:param df: DataFrame with "score" (need to be number), "is_leveled" and "score_rate" columns.
>>> import pandas as pd
>>> d = {"Eleve":["E1"]*6 + ["E2"]*6,
@ -113,12 +114,12 @@ def compute_normalized(df, rounding=lambda x: round(x, 2)):
>>> d = {"Eleve":["E1"]*6 + ["E2"]*6,
... "score_rate":[1]*2+[2]*2+[2]*2 + [1]*2+[2]*2+[2]*2,
... "is_leveled":[0]*4+[1]*2 + [0]*4+[1]*2,
... "score":[1, 0.33, 2, 1.5, 1, 3, 0.666, 1, 1.5, 1, 2, 3],
... "score":[0, 0.33, 2, 1.5, 1, 3, 0.666, 1, 1.5, 1, 2, 3],
... }
>>> df = pd.DataFrame(d)
>>> df["mark"] = compute_marks(df, 3)
>>> compute_normalized(df)
0 1.00
0 0.00
1 0.33
2 1.00
3 0.75
@ -135,6 +136,60 @@ def compute_normalized(df, rounding=lambda x: round(x, 2)):
return rounding(df["mark"] / df["score_rate"])
def filter_none_score(df, score_config):
"""Filter rows where scores have None numeric values
:example:
>>> import pandas as pd
>>> d = {"Eleve":["E1"]*7,
... "score_rate": [1]*7,
... "is_leveled":[0]+[1]*6,
... "score":[0.33, "", ".", "a", 1, 2, 3],
... }
>>> score_config = {
... 'BAD': {'value': 0, 'numeric_value': 0},
... 'FEW': {'value': 1, 'numeric_value': 1},
... 'NEARLY': {'value': 2, 'numeric_value': 2},
... 'GOOD': {'value': 3, 'numeric_value': 3},
... 'NOTFILLED': {'value': '', 'numeric_value': 'None'},
... 'NOANSWER': {'value': '.', 'numeric_value': 0},
... 'ABS': {'value': 'a', 'numeric_value': 'None'}
... }
>>> df = pd.DataFrame(d)
>>> filter_none_score(df, score_config)
Eleve score_rate is_leveled score
0 E1 1 0 0.33
2 E1 1 1 .
4 E1 1 1 1
5 E1 1 1 2
6 E1 1 1 3
"""
not_leveled_df = df[df["is_leveled"] != 1]
leveled_df = df[df["is_leveled"] == 1]
not_none_values = [
v["value"]
for v in score_config.values()
if str(v["numeric_value"]).lower() != "none"
]
filtered_leveled_df = leveled_df[leveled_df["score"].isin(not_none_values)]
return pd.concat([not_leveled_df, filtered_leveled_df])
def score_to_numeric_score(df, score_config):
"""Transform a score to the corresponding numeric value
>>> d = {"Eleve":["E1"]*6 + ["E2"]*6,
... "score_rate":[1]*2+[2]*2+[2]*2 + [1]*2+[2]*2+[2]*2,
... "is_leveled":[0]*4+[1]*2 + [0]*4+[1]*2,
... "score":[0, 0.33, 2, 1.5, 1, 3, 0.666, 1, 1.5, 1, 2, 3],
... }
"""
pass
# -----------------------------
# Reglages pour 'vim'
# vim:set autoindent expandtab tabstop=4 shiftwidth=4: